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https://paperswithcode.com/paper/a-study-of-question-effectiveness-using
|
1805.10389
| null | null |
A Study of Question Effectiveness Using Reddit "Ask Me Anything" Threads
|
Asking effective questions is a powerful social skill. In this paper we seek
to build computational models that learn to discriminate effective questions
from ineffective ones. Armed with such a capability, future advanced systems
can evaluate the quality of questions and provide suggestions for effective
question wording. We create a large-scale, real-world dataset that contains
over 400,000 questions collected from Reddit "Ask Me Anything" threads. Each
thread resembles an online press conference where questions compete with each
other for attention from the host. This dataset enables the development of a
class of computational models for predicting whether a question will be
answered. We develop a new convolutional neural network architecture with
variable-length context and demonstrate the efficacy of the model by comparing
it with state-of-the-art baselines and human judges.
| null |
http://arxiv.org/abs/1805.10389v1
|
http://arxiv.org/pdf/1805.10389v1.pdf
| null |
[
"Kristjan Arumae",
"Guo-Jun Qi",
"Fei Liu"
] |
[] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/mixed-precision-training-for-nlp-and-speech
|
1805.10387
| null | null |
Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq
|
We present OpenSeq2Seq - a TensorFlow-based toolkit for training
sequence-to-sequence models that features distributed and mixed-precision
training. Benchmarks on machine translation and speech recognition tasks show
that models built using OpenSeq2Seq give state-of-the-art performance at 1.5-3x
less training time. OpenSeq2Seq currently provides building blocks for models
that solve a wide range of tasks including neural machine translation,
automatic speech recognition, and speech synthesis.
|
We present OpenSeq2Seq - a TensorFlow-based toolkit for training sequence-to-sequence models that features distributed and mixed-precision training.
|
http://arxiv.org/abs/1805.10387v2
|
http://arxiv.org/pdf/1805.10387v2.pdf
| null |
[
"Oleksii Kuchaiev",
"Boris Ginsburg",
"Igor Gitman",
"Vitaly Lavrukhin",
"Jason Li",
"Huyen Nguyen",
"Carl Case",
"Paulius Micikevicius"
] |
[
"Automatic Speech Recognition",
"Automatic Speech Recognition (ASR)",
"Machine Translation",
"speech-recognition",
"Speech Recognition",
"Speech Synthesis",
"Translation"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/large-scale-distance-metric-learning-with
|
1805.10384
| null | null |
Large-scale Distance Metric Learning with Uncertainty
|
Distance metric learning (DML) has been studied extensively in the past
decades for its superior performance with distance-based algorithms. Most of
the existing methods propose to learn a distance metric with pairwise or
triplet constraints. However, the number of constraints is quadratic or even
cubic in the number of the original examples, which makes it challenging for
DML to handle the large-scale data set. Besides, the real-world data may
contain various uncertainty, especially for the image data. The uncertainty can
mislead the learning procedure and cause the performance degradation. By
investigating the image data, we find that the original data can be observed
from a small set of clean latent examples with different distortions. In this
work, we propose the margin preserving metric learning framework to learn the
distance metric and latent examples simultaneously. By leveraging the ideal
properties of latent examples, the training efficiency can be improved
significantly while the learned metric also becomes robust to the uncertainty
in the original data. Furthermore, we can show that the metric is learned from
latent examples only, but it can preserve the large margin property even for
the original data. The empirical study on the benchmark image data sets
demonstrates the efficacy and efficiency of the proposed method.
| null |
http://arxiv.org/abs/1805.10384v1
|
http://arxiv.org/pdf/1805.10384v1.pdf
|
CVPR 2018 6
|
[
"Qi Qian",
"Jiasheng Tang",
"Hao Li",
"Shenghuo Zhu",
"Rong Jin"
] |
[
"Metric Learning",
"Triplet"
] | 2018-05-25T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Qian_Large-Scale_Distance_Metric_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Qian_Large-Scale_Distance_Metric_CVPR_2018_paper.pdf
|
large-scale-distance-metric-learning-with-1
| null |
[] |
https://paperswithcode.com/paper/gradient-coding-via-the-stochastic-block
|
1805.10378
| null | null |
Gradient Coding via the Stochastic Block Model
|
Gradient descent and its many variants, including mini-batch stochastic
gradient descent, form the algorithmic foundation of modern large-scale machine
learning. Due to the size and scale of modern data, gradient computations are
often distributed across multiple compute nodes. Unfortunately, such
distributed implementations can face significant delays caused by straggler
nodes, i.e., nodes that are much slower than average. Gradient coding is a new
technique for mitigating the effect of stragglers via algorithmic redundancy.
While effective, previously proposed gradient codes can be computationally
expensive to construct, inaccurate, or susceptible to adversarial stragglers.
In this work, we present the stochastic block code (SBC), a gradient code based
on the stochastic block model. We show that SBCs are efficient, accurate, and
that under certain settings, adversarial straggler selection becomes as hard as
detecting a community structure in the multiple community, block stochastic
graph model.
| null |
http://arxiv.org/abs/1805.10378v1
|
http://arxiv.org/pdf/1805.10378v1.pdf
| null |
[
"Zachary Charles",
"Dimitris Papailiopoulos"
] |
[
"model",
"Stochastic Block Model"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-generative-dual-memory-network-for
|
1710.10368
| null |
BkVsWbbAW
|
Deep Generative Dual Memory Network for Continual Learning
|
Despite advances in deep learning, neural networks can only learn multiple
tasks when trained on them jointly. When tasks arrive sequentially, they lose
performance on previously learnt tasks. This phenomenon called catastrophic
forgetting is a fundamental challenge to overcome before neural networks can
learn continually from incoming data. In this work, we derive inspiration from
human memory to develop an architecture capable of learning continuously from
sequentially incoming tasks, while averting catastrophic forgetting.
Specifically, our contributions are: (i) a dual memory architecture emulating
the complementary learning systems (hippocampus and the neocortex) in the human
brain, (ii) memory consolidation via generative replay of past experiences,
(iii) demonstrating advantages of generative replay and dual memories via
experiments, and (iv) improved performance retention on challenging tasks even
for low capacity models. Our architecture displays many characteristics of the
mammalian memory and provides insights on the connection between sleep and
learning.
| null |
http://arxiv.org/abs/1710.10368v2
|
http://arxiv.org/pdf/1710.10368v2.pdf
|
ICLR 2018 1
|
[
"Nitin Kamra",
"Umang Gupta",
"Yan Liu"
] |
[
"Continual Learning",
"Hippocampus"
] | 2017-10-28T00:00:00 |
https://openreview.net/forum?id=BkVsWbbAW
|
https://openreview.net/pdf?id=BkVsWbbAW
|
deep-generative-dual-memory-network-for-1
| null |
[] |
https://paperswithcode.com/paper/ergodic-measure-preserving-flows
|
1805.10377
| null |
HkxZVlHYvH
|
Ergodic Inference: Accelerate Convergence by Optimisation
|
Statistical inference methods are fundamentally important in machine learning. Most state-of-the-art inference algorithms are variants of Markov chain Monte Carlo (MCMC) or variational inference (VI). However, both methods struggle with limitations in practice: MCMC methods can be computationally demanding; VI methods may have large bias. In this work, we aim to improve upon MCMC and VI by a novel hybrid method based on the idea of reducing simulation bias of finite-length MCMC chains using gradient-based optimisation. The proposed method can generate low-biased samples by increasing the length of MCMC simulation and optimising the MCMC hyper-parameters, which offers attractive balance between approximation bias and computational efficiency. We show that our method produces promising results on popular benchmarks when compared to recent hybrid methods of MCMC and VI.
| null |
https://arxiv.org/abs/1805.10377v4
|
https://arxiv.org/pdf/1805.10377v4.pdf
| null |
[
"Yichuan Zhang",
"José Miguel Hernández-Lobato"
] |
[
"Computational Efficiency",
"Variational Inference"
] | 2018-05-25T00:00:00 |
https://openreview.net/forum?id=HkxZVlHYvH
|
https://openreview.net/pdf?id=HkxZVlHYvH
| null | null |
[] |
https://paperswithcode.com/paper/robust-subspace-learning-robust-pca-robust
|
1711.09492
| null | null |
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery
|
PCA is one of the most widely used dimension reduction techniques. A related
easier problem is "subspace learning" or "subspace estimation". Given
relatively clean data, both are easily solved via singular value decomposition
(SVD). The problem of subspace learning or PCA in the presence of outliers is
called robust subspace learning or robust PCA (RPCA). For long data sequences,
if one tries to use a single lower dimensional subspace to represent the data,
the required subspace dimension may end up being quite large. For such data, a
better model is to assume that it lies in a low-dimensional subspace that can
change over time, albeit gradually. The problem of tracking such data (and the
subspaces) while being robust to outliers is called robust subspace tracking
(RST). This article provides a magazine-style overview of the entire field of
robust subspace learning and tracking. In particular solutions for three
problems are discussed in detail: RPCA via sparse+low-rank matrix decomposition
(S+LR), RST via S+LR, and "robust subspace recovery (RSR)". RSR assumes that an
entire data vector is either an outlier or an inlier. The S+LR formulation
instead assumes that outliers occur on only a few data vector indices and hence
are well modeled as sparse corruptions.
| null |
http://arxiv.org/abs/1711.09492v4
|
http://arxiv.org/pdf/1711.09492v4.pdf
| null |
[
"Namrata Vaswani",
"Thierry Bouwmans",
"Sajid Javed",
"Praneeth Narayanamurthy"
] |
[
"Dimensionality Reduction"
] | 2017-11-26T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Principle Components Analysis (PCA)** is an unsupervised method primary used for dimensionality reduction within machine learning. PCA is calculated via a singular value decomposition (SVD) of the design matrix, or alternatively, by calculating the covariance matrix of the data and performing eigenvalue decomposition on the covariance matrix. The results of PCA provide a low-dimensional picture of the structure of the data and the leading (uncorrelated) latent factors determining variation in the data.\r\n\r\nImage Source: [Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis#/media/File:GaussianScatterPCA.svg)",
"full_name": "Principal Components Analysis",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Dimensionality Reduction** methods transform data from a high-dimensional space into a low-dimensional space so that the low-dimensional space retains the most important properties of the original data. Below you can find a continuously updating list of dimensionality reduction methods.",
"name": "Dimensionality Reduction",
"parent": null
},
"name": "PCA",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/inference-in-probabilistic-graphical-models
|
1803.07710
| null | null |
Inference in Probabilistic Graphical Models by Graph Neural Networks
|
A fundamental computation for statistical inference and accurate decision-making is to compute the marginal probabilities or most probable states of task-relevant variables. Probabilistic graphical models can efficiently represent the structure of such complex data, but performing these inferences is generally difficult. Message-passing algorithms, such as belief propagation, are a natural way to disseminate evidence amongst correlated variables while exploiting the graph structure, but these algorithms can struggle when the conditional dependency graphs contain loops. Here we use Graph Neural Networks (GNNs) to learn a message-passing algorithm that solves these inference tasks. We first show that the architecture of GNNs is well-matched to inference tasks. We then demonstrate the efficacy of this inference approach by training GNNs on a collection of graphical models and showing that they substantially outperform belief propagation on loopy graphs. Our message-passing algorithms generalize out of the training set to larger graphs and graphs with different structure.
|
Message-passing algorithms, such as belief propagation, are a natural way to disseminate evidence amongst correlated variables while exploiting the graph structure, but these algorithms can struggle when the conditional dependency graphs contain loops.
|
https://arxiv.org/abs/1803.07710v5
|
https://arxiv.org/pdf/1803.07710v5.pdf
| null |
[
"KiJung Yoon",
"Renjie Liao",
"Yuwen Xiong",
"Lisa Zhang",
"Ethan Fetaya",
"Raquel Urtasun",
"Richard Zemel",
"Xaq Pitkow"
] |
[
"Decision Making"
] | 2018-03-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/static-and-dynamic-robust-pca-and-matrix
|
1803.00651
| null | null |
Static and Dynamic Robust PCA and Matrix Completion: A Review
|
Principal Components Analysis (PCA) is one of the most widely used dimension
reduction techniques. Robust PCA (RPCA) refers to the problem of PCA when the
data may be corrupted by outliers. Recent work by Cand{\`e}s, Wright, Li, and
Ma defined RPCA as a problem of decomposing a given data matrix into the sum of
a low-rank matrix (true data) and a sparse matrix (outliers). The column space
of the low-rank matrix then gives the PCA solution. This simple definition has
lead to a large amount of interesting new work on provably correct, fast, and
practical solutions to RPCA. More recently, the dynamic (time-varying) version
of the RPCA problem has been studied and a series of provably correct, fast,
and memory efficient tracking solutions have been proposed. Dynamic RPCA (or
robust subspace tracking) is the problem of tracking data lying in a (slowly)
changing subspace while being robust to sparse outliers. This article provides
an exhaustive review of the last decade of literature on RPCA and its dynamic
counterpart (robust subspace tracking), along with describing their theoretical
guarantees, discussing the pros and cons of various approaches, and providing
empirical comparisons of performance and speed.
A brief overview of the (low-rank) matrix completion literature is also
provided (the focus is on works not discussed in other recent reviews). This
refers to the problem of completing a low-rank matrix when only a subset of its
entries are observed. It can be interpreted as a simpler special case of RPCA
in which the indices of the outlier corrupted entries are known.
| null |
http://arxiv.org/abs/1803.00651v2
|
http://arxiv.org/pdf/1803.00651v2.pdf
| null |
[
"Namrata Vaswani",
"Praneeth Narayanamurthy"
] |
[
"Dimensionality Reduction",
"Low-Rank Matrix Completion",
"Matrix Completion"
] | 2018-03-01T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Principle Components Analysis (PCA)** is an unsupervised method primary used for dimensionality reduction within machine learning. PCA is calculated via a singular value decomposition (SVD) of the design matrix, or alternatively, by calculating the covariance matrix of the data and performing eigenvalue decomposition on the covariance matrix. The results of PCA provide a low-dimensional picture of the structure of the data and the leading (uncorrelated) latent factors determining variation in the data.\r\n\r\nImage Source: [Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis#/media/File:GaussianScatterPCA.svg)",
"full_name": "Principal Components Analysis",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Dimensionality Reduction** methods transform data from a high-dimensional space into a low-dimensional space so that the low-dimensional space retains the most important properties of the original data. Below you can find a continuously updating list of dimensionality reduction methods.",
"name": "Dimensionality Reduction",
"parent": null
},
"name": "PCA",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/heterogeneous-bitwidth-binarization-in
|
1805.10368
| null |
HJDV5YxCW
|
Heterogeneous Bitwidth Binarization in Convolutional Neural Networks
|
Recent work has shown that fast, compact low-bitwidth neural networks can be
surprisingly accurate. These networks use homogeneous binarization: all
parameters in each layer or (more commonly) the whole model have the same low
bitwidth (e.g., 2 bits). However, modern hardware allows efficient designs
where each arithmetic instruction can have a custom bitwidth, motivating
heterogeneous binarization, where every parameter in the network may have a
different bitwidth. In this paper, we show that it is feasible and useful to
select bitwidths at the parameter granularity during training. For instance a
heterogeneously quantized version of modern networks such as AlexNet and
MobileNet, with the right mix of 1-, 2- and 3-bit parameters that average to
just 1.4 bits can equal the accuracy of homogeneous 2-bit versions of these
networks. Further, we provide analyses to show that the heterogeneously
binarized systems yield FPGA- and ASIC-based implementations that are
correspondingly more efficient in both circuit area and energy efficiency than
their homogeneous counterparts.
| null |
http://arxiv.org/abs/1805.10368v2
|
http://arxiv.org/pdf/1805.10368v2.pdf
|
ICLR 2018 1
|
[
"Josh Fromm",
"Shwetak Patel",
"Matthai Philipose"
] |
[
"Binarization"
] | 2018-05-25T00:00:00 |
https://openreview.net/forum?id=HJDV5YxCW
|
https://openreview.net/pdf?id=HJDV5YxCW
|
heterogeneous-bitwidth-binarization-in-2
| null |
[
{
"code_snippet_url": "",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/1c5c289b6218eb1026dcb5fd9738231401cfccea/torch/nn/modules/normalization.py#L13",
"description": "**Local Response Normalization** is a normalization layer that implements the idea of lateral inhibition. Lateral inhibition is a concept in neurobiology that refers to the phenomenon of an excited neuron inhibiting its neighbours: this leads to a peak in the form of a local maximum, creating contrast in that area and increasing sensory perception. In practice, we can either normalize within the same channel or normalize across channels when we apply LRN to convolutional neural networks.\r\n\r\n$$ b_{c} = a_{c}\\left(k + \\frac{\\alpha}{n}\\sum_{c'=\\max(0, c-n/2)}^{\\min(N-1,c+n/2)}a_{c'}^2\\right)^{-\\beta} $$\r\n\r\nWhere the size is the number of neighbouring channels used for normalization, $\\alpha$ is multiplicative factor, $\\beta$ an exponent and $k$ an additive factor",
"full_name": "Local Response Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Local Response Normalization",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"code_snippet_url": "https://github.com/prlz77/ResNeXt.pytorch/blob/39fb8d03847f26ec02fb9b880ecaaa88db7a7d16/models/model.py#L42",
"description": "A **Grouped Convolution** uses a group of convolutions - multiple kernels per layer - resulting in multiple channel outputs per layer. This leads to wider networks helping a network learn a varied set of low level and high level features. The original motivation of using Grouped Convolutions in [AlexNet](https://paperswithcode.com/method/alexnet) was to distribute the model over multiple GPUs as an engineering compromise. But later, with models such as [ResNeXt](https://paperswithcode.com/method/resnext), it was shown this module could be used to improve classification accuracy. Specifically by exposing a new dimension through grouped convolutions, *cardinality* (the size of set of transformations), we can increase accuracy by increasing it.",
"full_name": "Grouped Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Grouped Convolution",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (a common value is $p=0.5$). At test time, all units are present, but with weights scaled by $p$ (i.e. $w$ becomes $pw$).\r\n\r\nThe idea is to prevent co-adaptation, where the neural network becomes too reliant on particular connections, as this could be symptomatic of overfitting. Intuitively, dropout can be thought of as creating an implicit ensemble of neural networks.",
"full_name": "Dropout",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "Regularization strategies are designed to reduce the test error of a machine learning algorithm, possibly at the expense of training error. Many different forms of regularization exist in the field of deep learning. Below you can find a constantly updating list of regularization strategies.",
"name": "Regularization",
"parent": null
},
"name": "Dropout",
"source_title": "Dropout: A Simple Way to Prevent Neural Networks from Overfitting",
"source_url": "http://jmlr.org/papers/v15/srivastava14a.html"
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/dansuh17/alexnet-pytorch/blob/d0c1b1c52296ffcbecfbf5b17e1d1685b4ca6744/model.py#L40",
"description": "To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.ggfdf\r\n\r\n\r\nHow do I speak to a person at Expedia?How do I speak to a person at Expedia?To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.\r\n\r\n\r\n\r\nTo make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.To make a reservation or communicate with Expedia, the quickest option is typically to call their customer service at +1-805-330-4056 or +1-805-330-4056. You can also use the live chat feature on their website or app, or contact them via social media.chgd",
"full_name": "How do I speak to a person at Expedia?-/+/",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "How do I speak to a person at Expedia?-/+/",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
}
] |
https://paperswithcode.com/paper/analysing-symbolic-regression-benchmarks
|
1805.10365
| null | null |
Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach
|
The definition of a concise and effective testbed for Genetic Programming
(GP) is a recurrent matter in the research community. This paper takes a new
step in this direction, proposing a different approach to measure the quality
of the symbolic regression benchmarks quantitatively. The proposed approach is
based on meta-learning and uses a set of dataset meta-features---such as the
number of examples or output skewness---to describe the datasets. Our idea is
to correlate these meta-features with the errors obtained by a GP method. These
meta-features define a space of benchmarks that should, ideally, have datasets
(points) covering different regions of the space. An initial analysis of 63
datasets showed that current benchmarks are concentrated in a small region of
this benchmark space. We also found out that number of instances and output
skewness are the most relevant meta-features to GP output error. Both
conclusions can help define which datasets should compose an effective testbed
for symbolic regression methods.
| null |
http://arxiv.org/abs/1805.10365v1
|
http://arxiv.org/pdf/1805.10365v1.pdf
| null |
[
"Luiz Otavio Vilas Boas Oliveira",
"Joao Francisco Barreto da Silva Martins",
"Luis Fernando Miranda",
"Gisele Lobo Pappa"
] |
[
"Meta-Learning",
"regression",
"Symbolic Regression"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/detecting-deceptive-reviews-using-generative
|
1805.10364
| null | null |
Detecting Deceptive Reviews using Generative Adversarial Networks
|
In the past few years, consumer review sites have become the main target of
deceptive opinion spam, where fictitious opinions or reviews are deliberately
written to sound authentic. Most of the existing work to detect the deceptive
reviews focus on building supervised classifiers based on syntactic and lexical
patterns of an opinion. With the successful use of Neural Networks on various
classification applications, in this paper, we propose FakeGAN a system that
for the first time augments and adopts Generative Adversarial Networks (GANs)
for a text classification task, in particular, detecting deceptive reviews.
Unlike standard GAN models which have a single Generator and Discriminator
model, FakeGAN uses two discriminator models and one generative model. The
generator is modeled as a stochastic policy agent in reinforcement learning
(RL), and the discriminators use Monte Carlo search algorithm to estimate and
pass the intermediate action-value as the RL reward to the generator. Providing
the generator model with two discriminator models avoids the mod collapse issue
by learning from both distributions of truthful and deceptive reviews. Indeed,
our experiments show that using two discriminators provides FakeGAN high
stability, which is a known issue for GAN architectures. While FakeGAN is built
upon a semi-supervised classifier, known for less accuracy, our evaluation
results on a dataset of TripAdvisor hotel reviews show the same performance in
terms of accuracy as of the state-of-the-art approaches that apply supervised
machine learning. These results indicate that GANs can be effective for text
classification tasks. Specifically, FakeGAN is effective at detecting deceptive
reviews.
| null |
http://arxiv.org/abs/1805.10364v1
|
http://arxiv.org/pdf/1805.10364v1.pdf
| null |
[
"Hojjat Aghakhani",
"Aravind Machiry",
"Shirin Nilizadeh",
"Christopher Kruegel",
"Giovanni Vigna"
] |
[
"General Classification",
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"text-classification",
"Text Classification"
] | 2018-05-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Dogecoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're trying to recover a lost Dogecoin wallet, knowing where to get help is essential. That’s why the Dogecoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Dogecoin Customer Support Number +1-833-534-1729\r\nDogecoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Dogecoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Dogecoin transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Dogecoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Dogecoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Dogecoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Dogecoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Dogecoin Deposit Not Received\r\nIf someone has sent you Dogecoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Dogecoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Dogecoin Transaction Stuck or Pending\r\nSometimes your Dogecoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Dogecoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Dogecoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Dogecoin Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Dogecoin tech.\r\n\r\n24/7 Availability: Dogecoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Dogecoin Support and Wallet Issues\r\nQ1: Can Dogecoin support help me recover stolen BTC?\r\nA: While Dogecoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Dogecoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Dogecoin’s official number (Dogecoin is decentralized), it connects you to trained professionals experienced in resolving all major Dogecoin issues.\r\n\r\nFinal Thoughts\r\nDogecoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Dogecoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Dogecoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Dogecoin Customer Service Number +1-833-534-1729",
"source_title": "Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1406.2661v1"
}
] |
https://paperswithcode.com/paper/provable-dynamic-robust-pca-or-robust
|
1705.08948
| null | null |
Provable Dynamic Robust PCA or Robust Subspace Tracking
|
Dynamic robust PCA refers to the dynamic (time-varying) extension of robust
PCA (RPCA). It assumes that the true (uncorrupted) data lies in a
low-dimensional subspace that can change with time, albeit slowly. The goal is
to track this changing subspace over time in the presence of sparse outliers.
We develop and study a novel algorithm, that we call simple-ReProCS, based on
the recently introduced Recursive Projected Compressive Sensing (ReProCS)
framework. Our work provides the first guarantee for dynamic RPCA that holds
under weakened versions of standard RPCA assumptions, slow subspace change and
a lower bound assumption on most outlier magnitudes. Our result is significant
because (i) it removes the strong assumptions needed by the two previous
complete guarantees for ReProCS-based algorithms; (ii) it shows that it is
possible to achieve significantly improved outlier tolerance, compared with all
existing RPCA or dynamic RPCA solutions by exploiting the above two simple
extra assumptions; and (iii) it proves that simple-ReProCS is online (after
initialization), fast, and, has near-optimal memory complexity.
|
Dynamic robust PCA refers to the dynamic (time-varying) extension of robust PCA (RPCA).
|
http://arxiv.org/abs/1705.08948v4
|
http://arxiv.org/pdf/1705.08948v4.pdf
| null |
[
"Praneeth Narayanamurthy",
"Namrata Vaswani"
] |
[
"Compressive Sensing"
] | 2017-05-24T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**Principle Components Analysis (PCA)** is an unsupervised method primary used for dimensionality reduction within machine learning. PCA is calculated via a singular value decomposition (SVD) of the design matrix, or alternatively, by calculating the covariance matrix of the data and performing eigenvalue decomposition on the covariance matrix. The results of PCA provide a low-dimensional picture of the structure of the data and the leading (uncorrelated) latent factors determining variation in the data.\r\n\r\nImage Source: [Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis#/media/File:GaussianScatterPCA.svg)",
"full_name": "Principal Components Analysis",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Dimensionality Reduction** methods transform data from a high-dimensional space into a low-dimensional space so that the low-dimensional space retains the most important properties of the original data. Below you can find a continuously updating list of dimensionality reduction methods.",
"name": "Dimensionality Reduction",
"parent": null
},
"name": "PCA",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/what-face-and-body-shapes-can-tell-about
|
1805.10355
| null | null |
What Face and Body Shapes Can Tell About Height
|
Recovering a person's height from a single image is important for virtual
garment fitting, autonomous driving and surveillance, however, it is also very
challenging due to the absence of absolute scale information. We tackle the
rarely addressed case, where camera parameters and scene geometry is unknown.
To nevertheless resolve the inherent scale ambiguity, we infer height from
statistics that are intrinsic to human anatomy and can be estimated from images
directly, such as articulated pose, bone length proportions, and facial
features. Our contribution is twofold. First, we experiment with different
machine learning models to capture the relation between image content and human
height. Second, we show that performance is predominantly limited by dataset
size and create a new dataset that is three magnitudes larger, by mining
explicit height labels and propagating them to additional images through face
recognition and assignment consistency. Our evaluation shows that monocular
height estimation is possible with a MAE of 5.56cm.
| null |
http://arxiv.org/abs/1805.10355v1
|
http://arxiv.org/pdf/1805.10355v1.pdf
| null |
[
"Semih Günel",
"Helge Rhodin",
"Pascal Fua"
] |
[
"Anatomy",
"Autonomous Driving",
"Face Recognition"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/self-net-lifelong-learning-via-continual-self
|
1805.10354
| null | null |
Self-Net: Lifelong Learning via Continual Self-Modeling
|
Learning a set of tasks over time, also known as continual learning (CL), is one of the most challenging problems in artificial intelligence. While recent approaches achieve some degree of CL in deep neural networks, they either (1) grow the network parameters linearly with the number of tasks, (2) require storing training data from previous tasks, or (3) restrict the network's ability to learn new tasks. To address these issues, we propose a novel framework, Self-Net, that uses an autoencoder to learn a set of low-dimensional representations of the weights learned for different tasks. We demonstrate that these low-dimensional vectors can then be used to generate high-fidelity recollections of the original weights. Self-Net can incorporate new tasks over time with little retraining and with minimal loss in performance for older tasks. Our system does not require storing prior training data and its parameters grow only logarithmically with the number of tasks. We show that our technique outperforms current state-of-the-art approaches on numerous datasets---including continual versions of MNIST, CIFAR10, CIFAR100, and Atari---and we demonstrate that our method can achieve over 10X storage compression in a continual fashion. To the best of our knowledge, we are the first to use autoencoders to sequentially encode sets of network weights to enable continual learning.
|
We demonstrate that these low-dimensional vectors can then be used to generate high-fidelity recollections of the original weights.
|
https://arxiv.org/abs/1805.10354v3
|
https://arxiv.org/pdf/1805.10354v3.pdf
| null |
[
"Blake Camp",
"Jaya Krishna Mandivarapu",
"Rolando Estrada"
] |
[
"Continual Learning",
"Lifelong learning"
] | 2018-05-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Solana transaction not confirmed, your Solana wallet not showing balance, or you're trying to recover a lost Solana wallet, knowing where to get help is essential. That’s why the Solana customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Solana Customer Support Number +1-833-534-1729\r\nSolana operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Solana Transaction Not Confirmed\r\nOne of the most common concerns is when a Solana transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Solana Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Solana wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Solana Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Solana wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Solana Deposit Not Received\r\nIf someone has sent you Solana but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Solana deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Solana Transaction Stuck or Pending\r\nSometimes your Solana transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Solana Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Solana wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Solana Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Solana tech.\r\n\r\n24/7 Availability: Solana doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Solana Support and Wallet Issues\r\nQ1: Can Solana support help me recover stolen BTC?\r\nA: While Solana transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Solana transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Solana’s official number (Solana is decentralized), it connects you to trained professionals experienced in resolving all major Solana issues.\r\n\r\nFinal Thoughts\r\nSolana is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Solana transaction not confirmed, your Solana wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Solana customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Solana Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Solana Customer Service Number +1-833-534-1729",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
}
] |
https://paperswithcode.com/paper/tensorial-neural-networks-generalization-of
|
1805.10352
| null | null |
Tensorial Neural Networks: Generalization of Neural Networks and Application to Model Compression
|
We propose tensorial neural networks (TNNs), a generalization of existing
neural networks by extending tensor operations on low order operands to those
on high order ones. The problem of parameter learning is challenging, as it
corresponds to hierarchical nonlinear tensor decomposition. We propose to solve
the learning problem using stochastic gradient descent by deriving nontrivial
backpropagation rules in generalized tensor algebra we introduce. Our proposed
TNNs has three advantages over existing neural networks: (1) TNNs naturally
apply to high order input object and thus preserve the multi-dimensional
structure in the input, as there is no need to flatten the data. (2) TNNs
interpret designs of existing neural network architectures. (3) Mapping a
neural network to TNNs with the same expressive power results in a TNN of fewer
parameters. TNN based compression of neural network improves existing low-rank
approximation based compression methods as TNNs exploit two other types of
invariant structures, periodicity and modulation, in addition to the low
rankness. Experiments on LeNet-5 (MNIST), ResNet-32 (CIFAR10) and ResNet-50
(ImageNet) demonstrate that our TNN based compression outperforms (5% test
accuracy improvement universally on CIFAR10) the state-of-the-art low-rank
approximation based compression methods under the same compression rate,
besides achieving orders of magnitude faster convergence rates due to the
efficiency of TNNs.
| null |
http://arxiv.org/abs/1805.10352v3
|
http://arxiv.org/pdf/1805.10352v3.pdf
| null |
[
"Jiahao Su",
"Jingling Li",
"Bobby Bhattacharjee",
"Furong Huang"
] |
[
"Model Compression",
"tensor algebra",
"Tensor Decomposition"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/guaranteed-simultaneous-asymmetric-tensor
|
1805.10348
| null | null |
Guaranteed Simultaneous Asymmetric Tensor Decomposition via Orthogonalized Alternating Least Squares
|
Tensor CANDECOMP/PARAFAC (CP) decomposition is an important tool that solves a wide class of machine learning problems. Existing popular approaches recover components one by one, not necessarily in the order of larger components first. Recently developed simultaneous power method obtains only a high probability recovery of top $r$ components even when the observed tensor is noiseless. We propose a Slicing Initialized Alternating Subspace Iteration (s-ASI) method that is guaranteed to recover top $r$ components ($\epsilon$-close) simultaneously for (a)symmetric tensors almost surely under the noiseless case (with high probability for a bounded noise) using $O(\log(\log \frac{1}{\epsilon}))$ steps of tensor subspace iterations. Our s-ASI method introduces a Slice-Based Initialization that runs $O(1/\log(\frac{\lambda_r}{\lambda_{r+1}}))$ steps of matrix subspace iterations, where $\lambda_r$ denotes the r-th top singular value of the tensor. We are the first to provide a theoretical guarantee on simultaneous orthogonal asymmetric tensor decomposition. Under the noiseless case, we are the first to provide an \emph{almost sure} theoretical guarantee on simultaneous orthogonal tensor decomposition. When tensor is noisy, our algorithm for asymmetric tensor is robust to noise smaller than $\min\{O(\frac{(\lambda_r - \lambda_{r+1})\epsilon}{\sqrt{r}}), O(\delta_0\frac{\lambda_r -\lambda_{r+1}}{\sqrt{d}})\}$, where $\delta_0$ is a small constant proportional to the probability of bad initializations in the noisy setting.
| null |
https://arxiv.org/abs/1805.10348v2
|
https://arxiv.org/pdf/1805.10348v2.pdf
| null |
[
"Furong Huang",
"Jialin Li",
"Xuchen You"
] |
[
"Tensor Decomposition"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-reduction-for-optimizing-lattice-submodular
|
1606.08362
| null | null |
A Reduction for Optimizing Lattice Submodular Functions with Diminishing Returns
|
A function $f: \mathbb{Z}_+^E \rightarrow \mathbb{R}_+$ is DR-submodular if
it satisfies $f({\bf x} + \chi_i) -f ({\bf x}) \ge f({\bf y} + \chi_i) - f({\bf
y})$ for all ${\bf x}\le {\bf y}, i\in E$. Recently, the problem of maximizing
a DR-submodular function $f: \mathbb{Z}_+^E \rightarrow \mathbb{R}_+$ subject
to a budget constraint $\|{\bf x}\|_1 \leq B$ as well as additional constraints
has received significant attention \cite{SKIK14,SY15,MYK15,SY16}.
In this note, we give a generic reduction from the DR-submodular setting to
the submodular setting. The running time of the reduction and the size of the
resulting submodular instance depends only \emph{logarithmically} on $B$. Using
this reduction, one can translate the results for unconstrained and constrained
submodular maximization to the DR-submodular setting for many types of
constraints in a unified manner.
| null |
http://arxiv.org/abs/1606.08362v2
|
http://arxiv.org/pdf/1606.08362v2.pdf
| null |
[
"Alina Ene",
"Huy L. Nguyen"
] |
[] | 2016-06-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/pathology-segmentation-using-distributional
|
1805.10344
| null | null |
Pathology Segmentation using Distributional Differences to Images of Healthy Origin
|
Fully supervised segmentation methods require a large training cohort of already segmented images, providing information at the pixel level of each image. We present a method to automatically segment and model pathologies in medical images, trained solely on data labelled on the image level as either healthy or containing a visual defect. We base our method on CycleGAN, an image-to-image translation technique, to translate images between the domains of healthy and pathological images. We extend the core idea with two key contributions. Implementing the generators as residual generators allows us to explicitly model the segmentation of the pathology. Realizing the translation from the healthy to the pathological domain using a variational autoencoder allows us to specify one representation of the pathology, as this transformation is otherwise not unique. Our model hence not only allows us to create pixelwise semantic segmentations, it is also able to create inpaintings for the segmentations to render the pathological image healthy. Furthermore, we can draw new unseen pathology samples from this model based on the distribution in the data. We show quantitatively, that our method is able to segment pathologies with a surprising accuracy being only slightly inferior to a state-of-the-art fully supervised method, although the latter has per-pixel rather than per-image training information. Moreover, we show qualitative results of both the segmentations and inpaintings. Our findings motivate further research into weakly-supervised segmentation using image level annotations, allowing for faster and cheaper acquisition of training data without a large sacrifice in segmentation accuracy.
| null |
https://arxiv.org/abs/1805.10344v2
|
https://arxiv.org/pdf/1805.10344v2.pdf
| null |
[
"Simon Andermatt",
"Antal Horváth",
"Simon Pezold",
"Philippe Cattin"
] |
[
"Image-to-Image Translation",
"Segmentation",
"Translation",
"Weakly supervised segmentation"
] | 2018-05-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/znxlwm/pytorch-pix2pix/blob/3059f2af53324e77089bbcfc31279f01a38c40b8/network.py#L104",
"description": "**PatchGAN** is a type of discriminator for generative adversarial networks which only penalizes structure at the scale of local image patches. The PatchGAN discriminator tries to classify if each $N \\times N$ patch in an image is real or fake. This discriminator is run convolutionally across the image, averaging all responses to provide the ultimate output of $D$. Such a discriminator effectively models the image as a Markov random field, assuming independence between pixels separated by more than a patch diameter. It can be understood as a type of texture/style loss.",
"full_name": "PatchGAN",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Discriminators** are a type of module used in architectures such as generative adversarial networks to discriminate between real and generated samples. Below you can find a continuously updating list of discriminators.",
"name": "Discriminators",
"parent": null
},
"name": "PatchGAN",
"source_title": "Image-to-Image Translation with Conditional Adversarial Networks",
"source_url": "http://arxiv.org/abs/1611.07004v3"
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nHistorically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neural networks. But it did not solve the vanishing gradient problem that sigmoids suffered, which was tackled more effectively with the introduction of [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nImage Source: [Junxi Feng](https://www.researchgate.net/profile/Junxi_Feng)",
"full_name": "Tanh Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Tanh Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L35",
"description": "**Residual Blocks** are skip-connection blocks that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture.\r\n \r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$. The $\\mathcal{F}({x})$ acts like a residual, hence the name 'residual block'.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers. Having skip connections allows the network to more easily learn identity-like mappings.\r\n\r\nNote that in practice, [Bottleneck Residual Blocks](https://paperswithcode.com/method/bottleneck-residual-block) are used for deeper ResNets, such as ResNet-50 and ResNet-101, as these bottleneck blocks are less computationally intensive.",
"full_name": "Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/1c5c289b6218eb1026dcb5fd9738231401cfccea/torch/nn/modules/instancenorm.py#L141",
"description": "**Instance Normalization** (also known as contrast normalization) is a normalization layer where:\r\n\r\n$$\r\n y_{tijk} = \\frac{x_{tijk} - \\mu_{ti}}{\\sqrt{\\sigma_{ti}^2 + \\epsilon}},\r\n \\quad\r\n \\mu_{ti} = \\frac{1}{HW}\\sum_{l=1}^W \\sum_{m=1}^H x_{tilm},\r\n \\quad\r\n \\sigma_{ti}^2 = \\frac{1}{HW}\\sum_{l=1}^W \\sum_{m=1}^H (x_{tilm} - \\mu_{ti})^2.\r\n$$\r\n\r\nThis prevents instance-specific mean and covariance shift simplifying the learning process. Intuitively, the normalization process allows to remove instance-specific contrast information from the content image in a task like image stylization, which simplifies generation.",
"full_name": "Instance Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Instance Normalization",
"source_title": "Instance Normalization: The Missing Ingredient for Fast Stylization",
"source_url": "http://arxiv.org/abs/1607.08022v3"
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "How do I get a human at Expedia?\r\nHow Do I Get a Human at Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Real-Time Help & Exclusive Travel Deals!Want to speak with a real person at Expedia? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now for immediate support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Skip the wait, get fast answers, and enjoy limited-time offers that make your next journey more affordable and stress-free. Call today and save!\r\n\r\nHow do I get a human at Expedia?\r\nHow Do I Get a Human at Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Real-Time Help & Exclusive Travel Deals!Want to speak with a real person at Expedia? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now for immediate support and unlock exclusive best deal discounts on flights, hotels, and vacation packages. Skip the wait, get fast answers, and enjoy limited-time offers that make your next journey more affordable and stress-free. Call today and save!",
"full_name": "HuMan(Expedia)||How do I get a human at Expedia?",
"introduced_year": 2014,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "HuMan(Expedia)||How do I get a human at Expedia?",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\\right)}$$\r\n\r\nSome drawbacks of this activation that have been noted in the literature are: sharp damp gradients during backpropagation from deeper hidden layers to inputs, gradient saturation, and slow convergence.",
"full_name": "Sigmoid Activation",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "Sigmoid Activation",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/eriklindernoren/PyTorch-GAN/blob/a163b82beff3d01688d8315a3fd39080400e7c01/implementations/lsgan/lsgan.py#L102",
"description": "**GAN Least Squares Loss** is a least squares loss function for generative adversarial networks. Minimizing this objective function is equivalent to minimizing the Pearson $\\chi^{2}$ divergence. The objective function (here for [LSGAN](https://paperswithcode.com/method/lsgan)) can be defined as:\r\n\r\n$$ \\min\\_{D}V\\_{LS}\\left(D\\right) = \\frac{1}{2}\\mathbb{E}\\_{\\mathbf{x} \\sim p\\_{data}\\left(\\mathbf{x}\\right)}\\left[\\left(D\\left(\\mathbf{x}\\right) - b\\right)^{2}\\right] + \\frac{1}{2}\\mathbb{E}\\_{\\mathbf{z}\\sim p\\_{data}\\left(\\mathbf{z}\\right)}\\left[\\left(D\\left(G\\left(\\mathbf{z}\\right)\\right) - a\\right)^{2}\\right] $$\r\n\r\n$$ \\min\\_{G}V\\_{LS}\\left(G\\right) = \\frac{1}{2}\\mathbb{E}\\_{\\mathbf{z} \\sim p\\_{\\mathbf{z}}\\left(\\mathbf{z}\\right)}\\left[\\left(D\\left(G\\left(\\mathbf{z}\\right)\\right) - c\\right)^{2}\\right] $$\r\n\r\nwhere $a$ and $b$ are the labels for fake data and real data and $c$ denotes the value that $G$ wants $D$ to believe for fake data.",
"full_name": "GAN Least Squares Loss",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Loss Functions** are used to frame the problem to be optimized within deep learning. Below you will find a continuously updating list of (specialized) loss functions for neutral networks.",
"name": "Loss Functions",
"parent": null
},
"name": "GAN Least Squares Loss",
"source_title": "Least Squares Generative Adversarial Networks",
"source_url": "http://arxiv.org/abs/1611.04076v3"
},
{
"code_snippet_url": "https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/f5834b3ed339ec268f40cf56928234eed8dfeb92/models/cycle_gan_model.py#L172",
"description": "**Cycle Consistency Loss** is a type of loss used for generative adversarial networks that performs unpaired image-to-image translation. It was introduced with the [CycleGAN](https://paperswithcode.com/method/cyclegan) architecture. For two domains $X$ and $Y$, we want to learn a mapping $G : X \\rightarrow Y$ and $F: Y \\rightarrow X$. We want to enforce the intuition that these mappings should be reverses of each other and that both mappings should be bijections. Cycle Consistency Loss encourages $F\\left(G\\left(x\\right)\\right) \\approx x$ and $G\\left(F\\left(y\\right)\\right) \\approx y$. It reduces the space of possible mapping functions by enforcing forward and backwards consistency:\r\n\r\n$$ \\mathcal{L}\\_{cyc}\\left(G, F\\right) = \\mathbb{E}\\_{x \\sim p\\_{data}\\left(x\\right)}\\left[||F\\left(G\\left(x\\right)\\right) - x||\\_{1}\\right] + \\mathbb{E}\\_{y \\sim p\\_{data}\\left(y\\right)}\\left[||G\\left(F\\left(y\\right)\\right) - y||\\_{1}\\right] $$",
"full_name": "Cycle Consistency Loss",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Loss Functions** are used to frame the problem to be optimized within deep learning. Below you will find a continuously updating list of (specialized) loss functions for neutral networks.",
"name": "Loss Functions",
"parent": null
},
"name": "Cycle Consistency Loss",
"source_title": "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks",
"source_url": "https://arxiv.org/abs/1703.10593v7"
},
{
"code_snippet_url": "https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9e6fff7b7d5215a38be3cac074ca7087041bea0d/models/cycle_gan_model.py#L8",
"description": "In today’s digital age, Cardano has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Cardano transaction not confirmed, your Cardano wallet not showing balance, or you're trying to recover a lost Cardano wallet, knowing where to get help is essential. That’s why the Cardano customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Cardano Customer Support Number +1-833-534-1729\r\nCardano operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Cardano Transaction Not Confirmed\r\nOne of the most common concerns is when a Cardano transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Cardano Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Cardano wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Cardano Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Cardano wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Cardano Deposit Not Received\r\nIf someone has sent you Cardano but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Cardano deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Cardano Transaction Stuck or Pending\r\nSometimes your Cardano transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Cardano Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Cardano wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Cardano Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Cardano tech.\r\n\r\n24/7 Availability: Cardano doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Cardano Support and Wallet Issues\r\nQ1: Can Cardano support help me recover stolen BTC?\r\nA: While Cardano transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Cardano transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Cardano’s official number (Cardano is decentralized), it connects you to trained professionals experienced in resolving all major Cardano issues.\r\n\r\nFinal Thoughts\r\nCardano is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Cardano transaction not confirmed, your Cardano wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Cardano customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Cardano Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Cardano Customer Service Number +1-833-534-1729",
"source_title": "Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks",
"source_url": "https://arxiv.org/abs/1703.10593v7"
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Solana transaction not confirmed, your Solana wallet not showing balance, or you're trying to recover a lost Solana wallet, knowing where to get help is essential. That’s why the Solana customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Solana Customer Support Number +1-833-534-1729\r\nSolana operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Solana Transaction Not Confirmed\r\nOne of the most common concerns is when a Solana transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Solana Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Solana wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Solana Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Solana wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Solana Deposit Not Received\r\nIf someone has sent you Solana but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Solana deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Solana Transaction Stuck or Pending\r\nSometimes your Solana transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Solana Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Solana wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Solana Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Solana tech.\r\n\r\n24/7 Availability: Solana doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Solana Support and Wallet Issues\r\nQ1: Can Solana support help me recover stolen BTC?\r\nA: While Solana transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Solana transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Solana’s official number (Solana is decentralized), it connects you to trained professionals experienced in resolving all major Solana issues.\r\n\r\nFinal Thoughts\r\nSolana is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Solana transaction not confirmed, your Solana wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Solana customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Solana Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Solana Customer Service Number +1-833-534-1729",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
}
] |
https://paperswithcode.com/paper/an-end-to-end-differentially-private-latent
|
1805.10341
| null | null |
An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm
|
We provide an end-to-end differentially private spectral algorithm for learning LDA, based on matrix/tensor decompositions, and establish theoretical guarantees on utility/consistency of the estimated model parameters. The spectral algorithm consists of multiple algorithmic steps, named as "{edges}", to which noise could be injected to obtain differential privacy. We identify \emph{subsets of edges}, named as "{configurations}", such that adding noise to all edges in such a subset guarantees differential privacy of the end-to-end spectral algorithm. We characterize the sensitivity of the edges with respect to the input and thus estimate the amount of noise to be added to each edge for any required privacy level. We then characterize the utility loss for each configuration as a function of injected noise. Overall, by combining the sensitivity and utility characterization, we obtain an end-to-end differentially private spectral algorithm for LDA and identify the corresponding configuration that outperforms others in any specific regime. We are the first to achieve utility guarantees under the required level of differential privacy for learning in LDA. Overall our method systematically outperforms differentially private variational inference.
| null |
https://arxiv.org/abs/1805.10341v3
|
https://arxiv.org/pdf/1805.10341v3.pdf
|
ICML 2020 1
|
[
"Christopher DeCarolis",
"Mukul Ram",
"Seyed A. Esmaeili",
"Yu-Xiang Wang",
"Furong Huang"
] |
[
"Sensitivity",
"Variational Inference"
] | 2018-05-25T00:00:00 |
https://proceedings.icml.cc/static/paper_files/icml/2020/2863-Paper.pdf
|
https://proceedings.icml.cc/static/paper_files/icml/2020/2863-Paper.pdf
| null | null |
[
{
"code_snippet_url": null,
"description": "**Linear discriminant analysis** (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.\r\n\r\nExtracted from [Wikipedia](https://en.wikipedia.org/wiki/Linear_discriminant_analysis)\r\n\r\n**Source**:\r\n\r\nPaper: [Linear Discriminant Analysis: A Detailed Tutorial](https://dx.doi.org/10.3233/AIC-170729)\r\n\r\nPublic version: [Linear Discriminant Analysis: A Detailed Tutorial](https://usir.salford.ac.uk/id/eprint/52074/)",
"full_name": "Linear Discriminant Analysis",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Dimensionality Reduction** methods transform data from a high-dimensional space into a low-dimensional space so that the low-dimensional space retains the most important properties of the original data. Below you can find a continuously updating list of dimensionality reduction methods.",
"name": "Dimensionality Reduction",
"parent": null
},
"name": "LDA",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/zero-shot-dual-machine-translation
|
1805.10338
| null |
ByecAoAqK7
|
Zero-Shot Dual Machine Translation
|
Neural Machine Translation (NMT) systems rely on large amounts of parallel
data. This is a major challenge for low-resource languages. Building on recent
work on unsupervised and semi-supervised methods, we present an approach that
combines zero-shot and dual learning. The latter relies on reinforcement
learning, to exploit the duality of the machine translation task, and requires
only monolingual data for the target language pair. Experiments show that a
zero-shot dual system, trained on English-French and English-Spanish,
outperforms by large margins a standard NMT system in zero-shot translation
performance on Spanish-French (both directions). The zero-shot dual method
approaches the performance, within 2.2 BLEU points, of a comparable supervised
setting. Our method can obtain improvements also on the setting where a small
amount of parallel data for the zero-shot language pair is available. Adding
Russian, to extend our experiments to jointly modeling 6 zero-shot translation
directions, all directions improve between 4 and 15 BLEU points, again,
reaching performance near that of the supervised setting.
|
Our method can obtain improvements also on the setting where a small amount of parallel data for the zero-shot language pair is available.
|
http://arxiv.org/abs/1805.10338v1
|
http://arxiv.org/pdf/1805.10338v1.pdf
| null |
[
"Lierni Sestorain",
"Massimiliano Ciaramita",
"Christian Buck",
"Thomas Hofmann"
] |
[
"Machine Translation",
"NMT",
"Reinforcement Learning",
"Translation"
] | 2018-05-25T00:00:00 |
https://openreview.net/forum?id=ByecAoAqK7
|
https://openreview.net/pdf?id=ByecAoAqK7
|
zero-shot-dual-machine-translation-1
| null |
[] |
https://paperswithcode.com/paper/unlearn-what-you-have-learned-adaptive-crowd
|
1804.06481
| null | null |
Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners
|
With the increasing demand for large amount of labeled data, crowdsourcing
has been used in many large-scale data mining applications. However, most
existing works in crowdsourcing mainly focus on label inference and incentive
design. In this paper, we address a different problem of adaptive crowd
teaching, which is a sub-area of machine teaching in the context of
crowdsourcing. Compared with machines, human beings are extremely good at
learning a specific target concept (e.g., classifying the images into given
categories) and they can also easily transfer the learned concepts into similar
learning tasks. Therefore, a more effective way of utilizing crowdsourcing is
by supervising the crowd to label in the form of teaching. In order to perform
the teaching and expertise estimation simultaneously, we propose an adaptive
teaching framework named JEDI to construct the personalized optimal teaching
set for the crowdsourcing workers. In JEDI teaching, the teacher assumes that
each learner has an exponentially decayed memory. Furthermore, it ensures
comprehensiveness in the learning process by carefully balancing teaching
diversity and learner's accurate learning in terms of teaching usefulness.
Finally, we validate the effectiveness and efficacy of JEDI teaching in
comparison with the state-of-the-art techniques on multiple data sets with both
synthetic learners and real crowdsourcing workers.
|
With the increasing demand for large amount of labeled data, crowdsourcing has been used in many large-scale data mining applications.
|
http://arxiv.org/abs/1804.06481v2
|
http://arxiv.org/pdf/1804.06481v2.pdf
| null |
[
"Yao Zhou",
"Arun Reddy Nelakurthi",
"Jingrui He"
] |
[
"Diversity"
] | 2018-04-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/towards-deep-cellular-phenotyping-in
|
1804.03270
| null | null |
Towards Deep Cellular Phenotyping in Placental Histology
|
The placenta is a complex organ, playing multiple roles during fetal
development. Very little is known about the association between placental
morphological abnormalities and fetal physiology. In this work, we present an
open sourced, computationally tractable deep learning pipeline to analyse
placenta histology at the level of the cell. By utilising two deep
Convolutional Neural Network architectures and transfer learning, we can
robustly localise and classify placental cells within five classes with an
accuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic
knowledge that is capable of both stratifying five distinct cell populations
and learn intraclass phenotypic variance. We envisage that the automation of
this pipeline to population scale studies of placenta histology has the
potential to improve our understanding of basic cellular placental biology and
its variations, particularly its role in predicting adverse birth outcomes.
|
The placenta is a complex organ, playing multiple roles during fetal development.
|
http://arxiv.org/abs/1804.03270v2
|
http://arxiv.org/pdf/1804.03270v2.pdf
| null |
[
"Michael Ferlaino",
"Craig A. Glastonbury",
"Carolina Motta-Mejia",
"Manu Vatish",
"Ingrid Granne",
"Stephen Kennedy",
"Cecilia M. Lindgren",
"Christoffer Nellåker"
] |
[
"Transfer Learning"
] | 2018-04-09T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/enhancing-the-accuracy-and-fairness-of-human
|
1805.10318
| null | null |
Enhancing the Accuracy and Fairness of Human Decision Making
|
Societies often rely on human experts to take a wide variety of decisions
affecting their members, from jail-or-release decisions taken by judges and
stop-and-frisk decisions taken by police officers to accept-or-reject decisions
taken by academics. In this context, each decision is taken by an expert who is
typically chosen uniformly at random from a pool of experts. However, these
decisions may be imperfect due to limited experience, implicit biases, or
faulty probabilistic reasoning. Can we improve the accuracy and fairness of the
overall decision making process by optimizing the assignment between experts
and decisions?
In this paper, we address the above problem from the perspective of
sequential decision making and show that, for different fairness notions from
the literature, it reduces to a sequence of (constrained) weighted bipartite
matchings, which can be solved efficiently using algorithms with approximation
guarantees. Moreover, these algorithms also benefit from posterior sampling to
actively trade off exploitation---selecting expert assignments which lead to
accurate and fair decisions---and exploration---selecting expert assignments to
learn about the experts' preferences and biases. We demonstrate the
effectiveness of our algorithms on both synthetic and real-world data and show
that they can significantly improve both the accuracy and fairness of the
decisions taken by pools of experts.
|
Societies often rely on human experts to take a wide variety of decisions affecting their members, from jail-or-release decisions taken by judges and stop-and-frisk decisions taken by police officers to accept-or-reject decisions taken by academics.
|
http://arxiv.org/abs/1805.10318v1
|
http://arxiv.org/pdf/1805.10318v1.pdf
|
NeurIPS 2018 12
|
[
"Isabel Valera",
"Adish Singla",
"Manuel Gomez Rodriguez"
] |
[
"Decision Making",
"Fairness",
"Sequential Decision Making"
] | 2018-05-25T00:00:00 |
http://papers.nips.cc/paper/7448-enhancing-the-accuracy-and-fairness-of-human-decision-making
|
http://papers.nips.cc/paper/7448-enhancing-the-accuracy-and-fairness-of-human-decision-making.pdf
|
enhancing-the-accuracy-and-fairness-of-human-1
| null |
[] |
https://paperswithcode.com/paper/organics-a-theory-of-working-memory-in-brains
|
1803.06288
| null | null |
ORGaNICs: A Theory of Working Memory in Brains and Machines
|
Working memory is a cognitive process that is responsible for temporarily
holding and manipulating information. Most of the empirical neuroscience
research on working memory has focused on measuring sustained activity in
prefrontal cortex (PFC) and/or parietal cortex during simple delayed-response
tasks, and most of the models of working memory have been based on neural
integrators. But working memory means much more than just holding a piece of
information online. We describe a new theory of working memory, based on a
recurrent neural circuit that we call ORGaNICs (Oscillatory Recurrent GAted
Neural Integrator Circuits). ORGaNICs are a variety of Long Short Term Memory
units (LSTMs), imported from machine learning and artificial intelligence.
ORGaNICs can be used to explain the complex dynamics of delay-period activity
in prefrontal cortex (PFC) during a working memory task. The theory is
analytically tractable so that we can characterize the dynamics, and the theory
provides a means for reading out information from the dynamically varying
responses at any point in time, in spite of the complex dynamics. ORGaNICs can
be implemented with a biophysical (electrical circuit) model of pyramidal
cells, combined with shunting inhibition via a thalamocortical loop. Although
introduced as a computational theory of working memory, ORGaNICs are also
applicable to models of sensory processing, motor preparation and motor
control. ORGaNICs offer computational advantages compared to other varieties of
LSTMs that are commonly used in AI applications. Consequently, ORGaNICs are a
framework for canonical computation in brains and machines.
| null |
http://arxiv.org/abs/1803.06288v4
|
http://arxiv.org/pdf/1803.06288v4.pdf
| null |
[
"David J. Heeger",
"Wayne E. Mackey"
] |
[] | 2018-03-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-self-imitating-diverse-policies
|
1805.10309
| null |
HyxzRsR9Y7
|
Learning Self-Imitating Diverse Policies
|
The success of popular algorithms for deep reinforcement learning, such as
policy-gradients and Q-learning, relies heavily on the availability of an
informative reward signal at each timestep of the sequential decision-making
process. When rewards are only sparsely available during an episode, or a
rewarding feedback is provided only after episode termination, these algorithms
perform sub-optimally due to the difficultly in credit assignment.
Alternatively, trajectory-based policy optimization methods, such as
cross-entropy method and evolution strategies, do not require per-timestep
rewards, but have been found to suffer from high sample complexity by
completing forgoing the temporal nature of the problem. Improving the
efficiency of RL algorithms in real-world problems with sparse or episodic
rewards is therefore a pressing need. In this work, we introduce a
self-imitation learning algorithm that exploits and explores well in the sparse
and episodic reward settings. We view each policy as a state-action visitation
distribution and formulate policy optimization as a divergence minimization
problem. We show that with Jensen-Shannon divergence, this divergence
minimization problem can be reduced into a policy-gradient algorithm with
shaped rewards learned from experience replays. Experimental results indicate
that our algorithm works comparable to existing algorithms in environments with
dense rewards, and significantly better in environments with sparse and
episodic rewards. We then discuss limitations of self-imitation learning, and
propose to solve them by using Stein variational policy gradient descent with
the Jensen-Shannon kernel to learn multiple diverse policies. We demonstrate
its effectiveness on a challenging variant of continuous-control MuJoCo
locomotion tasks.
| null |
http://arxiv.org/abs/1805.10309v2
|
http://arxiv.org/pdf/1805.10309v2.pdf
|
ICLR 2019 5
|
[
"Tanmay Gangwani",
"Qiang Liu",
"Jian Peng"
] |
[
"continuous-control",
"Continuous Control",
"Decision Making",
"Deep Reinforcement Learning",
"Imitation Learning",
"MuJoCo",
"Policy Gradient Methods",
"Q-Learning",
"Reinforcement Learning",
"Sequential Decision Making"
] | 2018-05-25T00:00:00 |
https://openreview.net/forum?id=HyxzRsR9Y7
|
https://openreview.net/pdf?id=HyxzRsR9Y7
|
learning-self-imitating-diverse-policies-1
| null |
[] |
https://paperswithcode.com/paper/semantic-binary-segmentation-using
|
1805.00138
| null | null |
Semantic Binary Segmentation using Convolutional Networks without Decoders
|
In this paper, we propose an efficient architecture for semantic image
segmentation using the depth-to-space (D2S) operation. Our D2S model is
comprised of a standard CNN encoder followed by a depth-to-space reordering of
the final convolutional feature maps. Our approach eliminates the decoder
portion of traditional encoder-decoder segmentation models and reduces the
amount of computation almost by half. As a participant of the DeepGlobe Road
Extraction competition, we evaluate our models on the corresponding road
segmentation dataset. Our highly efficient D2S models exhibit comparable
performance to standard segmentation models with much lower computational cost.
|
In this paper, we propose an efficient architecture for semantic image segmentation using the depth-to-space (D2S) operation.
|
http://arxiv.org/abs/1805.00138v2
|
http://arxiv.org/pdf/1805.00138v2.pdf
| null |
[
"Shubhra Aich",
"William van der Kamp",
"Ian Stavness"
] |
[
"Decoder",
"Image Segmentation",
"Road Segmentation",
"Segmentation",
"Semantic Segmentation"
] | 2018-05-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/forecasting-the-successful-execution-of
|
1805.10307
| null | null |
Forecasting the successful execution of horizontal strategy in a diversified corporation via a DEMATEL-supported artificial neural network - A case study
|
Nowadays, competition is getting tougher as market shrinks because of
financial crisis of the late 2000s. Organizations are tensely forced to
leverage their core competencies to survive through attracting more customers
and gaining more efficacious operations. In such a situation, diversified
corporations which run multiple businesses have opportunities to get
competitive advantage and differentiate themselves by executing horizontal
strategy. Since this strategy completely engages a number of business units of
a diversified corporation through resource sharing among them, any effort to
implement it will fail if being not supported by enough information. However,
for successful execution of horizontal strategy, managers should have reliable
information concerning its success probability in advance. To provide such a
precious information, a three-step framework has been developed. In the first
step, major influencers on successful execution of horizontal strategy have
been captured through literature study and interviewing subject matter experts.
In the second step through the decision making trial and evaluation laboratory
(DEMATEL) methodology, critical success factors (CSFs) have been extracted from
major influencers and a success probability assessment index system (SPAIS) has
been formed. In the third step, due to the statistical nature (multivariate and
distribution free) of SPAIS, an artificial neural network has been designed for
enabling organizational managers to forecast the success probability of
horizontal strategy execution in a multi-business corporation far better than
other classical models.
| null |
http://arxiv.org/abs/1805.10307v1
|
http://arxiv.org/pdf/1805.10307v1.pdf
| null |
[
"Hossein Sabzian",
"Hossein Gharib",
"Javad Noori",
"Mohammad Ali Shafia",
"Mohammad Javad Sheikh"
] |
[
"Decision Making"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/umdsub-at-semeval-2018-task-2-multilingual
|
1805.10274
| null | null |
UMDSub at SemEval-2018 Task 2: Multilingual Emoji Prediction Multi-channel Convolutional Neural Network on Subword Embedding
|
This paper describes the UMDSub system that participated in Task 2 of
SemEval-2018. We developed a system that predicts an emoji given the raw text
in a English tweet. The system is a Multi-channel Convolutional Neural Network
based on subword embeddings for the representation of tweets. This model
improves on character or word based methods by about 2\%. Our system placed
21st of 48 participating systems in the official evaluation.
| null |
http://arxiv.org/abs/1805.10274v1
|
http://arxiv.org/pdf/1805.10274v1.pdf
|
SEMEVAL 2018 6
|
[
"Zhenduo Wang",
"Ted Pedersen"
] |
[
"Task 2"
] | 2018-05-25T00:00:00 |
https://aclanthology.org/S18-1060
|
https://aclanthology.org/S18-1060.pdf
|
umdsub-at-semeval-2018-task-2-multilingual-1
| null |
[] |
https://paperswithcode.com/paper/umduluth-cs8761-at-semeval-2018-task-9
|
1805.10271
| null | null |
UMDuluth-CS8761 at SemEval-2018 Task 9: Hypernym Discovery using Hearst Patterns, Co-occurrence frequencies and Word Embeddings
|
Hypernym Discovery is the task of identifying potential hypernyms for a given
term. A hypernym is a more generalized word that is super-ordinate to more
specific words. This paper explores several approaches that rely on
co-occurrence frequencies of word pairs, Hearst Patterns based on regular
expressions, and word embeddings created from the UMBC corpus. Our system
Babbage participated in Subtask 1A for English and placed 6th of 19 systems
when identifying concept hypernyms, and 12th of 18 systems for entity
hypernyms.
| null |
http://arxiv.org/abs/1805.10271v1
|
http://arxiv.org/pdf/1805.10271v1.pdf
| null |
[
"Arshia Z. Hassan",
"Manikya S. Vallabhajosyula",
"Ted Pedersen"
] |
[
"Hypernym Discovery",
"Word Embeddings"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/duluth-urop-at-semeval-2018-task-2
|
1805.10267
| null | null |
Duluth UROP at SemEval-2018 Task 2: Multilingual Emoji Prediction with Ensemble Learning and Oversampling
|
This paper describes the Duluth UROP systems that participated in
SemEval--2018 Task 2, Multilingual Emoji Prediction. We relied on a variety of
ensembles made up of classifiers using Naive Bayes, Logistic Regression, and
Random Forests. We used unigram and bigram features and tried to offset the
skewness of the data through the use of oversampling. Our task evaluation
results place us 19th of 48 systems in the English evaluation, and 5th of 21 in
the Spanish. After the evaluation we realized that some simple changes to
preprocessing could significantly improve our results. After making these
changes we attained results that would have placed us sixth in the English
evaluation, and second in the Spanish.
|
Our task evaluation results place us 19th of 48 systems in the English evaluation, and 5th of 21 in the Spanish.
|
http://arxiv.org/abs/1805.10267v1
|
http://arxiv.org/pdf/1805.10267v1.pdf
|
SEMEVAL 2018 6
|
[
"Shuning Jin",
"Ted Pedersen"
] |
[
"Ensemble Learning",
"regression",
"Task 2"
] | 2018-05-25T00:00:00 |
https://aclanthology.org/S18-1077
|
https://aclanthology.org/S18-1077.pdf
|
duluth-urop-at-semeval-2018-task-2-1
| null |
[
{
"code_snippet_url": null,
"description": "**Logistic Regression**, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function.\r\n\r\nSource: [scikit-learn](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)\r\n\r\nImage: [Michaelg2015](https://commons.wikimedia.org/wiki/User:Michaelg2015)",
"full_name": "Logistic Regression",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Generalized Linear Models (GLMs)** are a class of models that generalize upon linear regression by allowing many more distributions to be modeled for the response variable via a link function. Below you can find a continuously updating list of GLMs.",
"name": "Generalized Linear Models",
"parent": null
},
"name": "Logistic Regression",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/learning-restricted-boltzmann-machines-via
|
1805.10262
| null | null |
Learning Restricted Boltzmann Machines via Influence Maximization
|
Graphical models are a rich language for describing high-dimensional
distributions in terms of their dependence structure. While there are
algorithms with provable guarantees for learning undirected graphical models in
a variety of settings, there has been much less progress in the important
scenario when there are latent variables. Here we study Restricted Boltzmann
Machines (or RBMs), which are a popular model with wide-ranging applications in
dimensionality reduction, collaborative filtering, topic modeling, feature
extraction and deep learning.
The main message of our paper is a strong dichotomy in the feasibility of
learning RBMs, depending on the nature of the interactions between variables:
ferromagnetic models can be learned efficiently, while general models cannot.
In particular, we give a simple greedy algorithm based on influence
maximization to learn ferromagnetic RBMs with bounded degree. In fact, we learn
a description of the distribution on the observed variables as a Markov Random
Field. Our analysis is based on tools from mathematical physics that were
developed to show the concavity of magnetization. Our algorithm extends
straighforwardly to general ferromagnetic Ising models with latent variables.
Conversely, we show that even for a contant number of latent variables with
constant degree, without ferromagneticity the problem is as hard as sparse
parity with noise. This hardness result is based on a sharp and surprising
characterization of the representational power of bounded degree RBMs: the
distribution on their observed variables can simulate any bounded order MRF.
This result is of independent interest since RBMs are the building blocks of
deep belief networks.
| null |
http://arxiv.org/abs/1805.10262v2
|
http://arxiv.org/pdf/1805.10262v2.pdf
| null |
[
"Guy Bresler",
"Frederic Koehler",
"Ankur Moitra",
"Elchanan Mossel"
] |
[
"Collaborative Filtering",
"Dimensionality Reduction"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/optimal-bayesian-transfer-learning
|
1801.00857
| null | null |
Optimal Bayesian Transfer Learning
|
Transfer learning has recently attracted significant research attention, as
it simultaneously learns from different source domains, which have plenty of
labeled data, and transfers the relevant knowledge to the target domain with
limited labeled data to improve the prediction performance. We propose a
Bayesian transfer learning framework where the source and target domains are
related through the joint prior density of the model parameters. The modeling
of joint prior densities enables better understanding of the "transferability"
between domains. We define a joint Wishart density for the precision matrices
of the Gaussian feature-label distributions in the source and target domains to
act like a bridge that transfers the useful information of the source domain to
help classification in the target domain by improving the target posteriors.
Using several theorems in multivariate statistics, the posteriors and posterior
predictive densities are derived in closed forms with hypergeometric functions
of matrix argument, leading to our novel closed-form and fast Optimal Bayesian
Transfer Learning (OBTL) classifier. Experimental results on both synthetic and
real-world benchmark data confirm the superb performance of the OBTL compared
to the other state-of-the-art transfer learning and domain adaptation methods.
| null |
http://arxiv.org/abs/1801.00857v2
|
http://arxiv.org/pdf/1801.00857v2.pdf
| null |
[
"Alireza Karbalayghareh",
"Xiaoning Qian",
"Edward R. Dougherty"
] |
[
"Domain Adaptation",
"Transfer Learning"
] | 2018-01-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/randomized-robust-matrix-completion-for-the
|
1805.10927
| null | null |
Scalable and Robust Community Detection with Randomized Sketching
|
This article explores and analyzes the unsupervised clustering of large partially observed graphs. We propose a scalable and provable randomized framework for clustering graphs generated from the stochastic block model. The clustering is first applied to a sub-matrix of the graph's adjacency matrix associated with a reduced graph sketch constructed using random sampling. Then, the clusters of the full graph are inferred based on the clusters extracted from the sketch using a correlation-based retrieval step. Uniform random node sampling is shown to improve the computational complexity over clustering of the full graph when the cluster sizes are balanced. A new random degree-based node sampling algorithm is presented which significantly improves upon the performance of the clustering algorithm even when clusters are unbalanced. This framework improves the phase transitions for matrix-decomposition-based clustering with regard to computational complexity and minimum cluster size, which are shown to be nearly dimension-free in the low inter-cluster connectivity regime. A third sampling technique is shown to improve balance by randomly sampling nodes based on spatial distribution. We provide analysis and numerical results using a convex clustering algorithm based on matrix completion.
| null |
https://arxiv.org/abs/1805.10927v4
|
https://arxiv.org/pdf/1805.10927v4.pdf
| null |
[
"Mostafa Rahmani",
"Andre Beckus",
"Adel Karimian",
"George Atia"
] |
[
"Clustering",
"Community Detection",
"Matrix Completion",
"Retrieval",
"Stochastic Block Model"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/unsupervised-learning-for-large-scale-fiber
|
1805.10256
| null | null |
Unsupervised Learning for Large-Scale Fiber Detection and Tracking in Microscopic Material Images
|
Constructing 3D structures from serial section data is a long standing
problem in microscopy. The structure of a fiber reinforced composite material
can be reconstructed using a tracking-by-detection model. Tracking-by-detection
algorithms rely heavily on detection accuracy, especially the recall
performance. The state-of-the-art fiber detection algorithms perform well under
ideal conditions, but are not accurate where there are local degradations of
image quality, due to contaminants on the material surface and/or defocus blur.
Convolutional Neural Networks (CNN) could be used for this problem, but would
require a large number of manual annotated fibers, which are not available. We
propose an unsupervised learning method to accurately detect fibers on the
large scale, that is robust against local degradations of image quality. The
proposed method does not require manual annotations, but uses fiber shape/size
priors and spatio-temporal consistency in tracking to simulate the supervision
in the training of the CNN. Experiments show significant improvements over
state-of-the-art fiber detection algorithms together with advanced tracking
performance.
| null |
http://arxiv.org/abs/1805.10256v1
|
http://arxiv.org/pdf/1805.10256v1.pdf
| null |
[
"Hongkai Yu",
"Dazhou Guo",
"Zhipeng Yan",
"Wei Liu",
"Jeff Simmons",
"Craig P. Przybyla",
"Song Wang"
] |
[] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/parallel-architecture-and-hyperparameter
|
1805.10255
| null | null |
Parallel Architecture and Hyperparameter Search via Successive Halving and Classification
|
We present a simple and powerful algorithm for parallel black box
optimization called Successive Halving and Classification (SHAC). The algorithm
operates in $K$ stages of parallel function evaluations and trains a cascade of
binary classifiers to iteratively cull the undesirable regions of the search
space. SHAC is easy to implement, requires no tuning of its own configuration
parameters, is invariant to the scale of the objective function and can be
built using any choice of binary classifier. We adopt tree-based classifiers
within SHAC and achieve competitive performance against several strong
baselines for optimizing synthetic functions, hyperparameters and
architectures.
|
We present a simple and powerful algorithm for parallel black box optimization called Successive Halving and Classification (SHAC).
|
http://arxiv.org/abs/1805.10255v1
|
http://arxiv.org/pdf/1805.10255v1.pdf
| null |
[
"Manoj Kumar",
"George E. Dahl",
"Vijay Vasudevan",
"Mohammad Norouzi"
] |
[
"Classification",
"General Classification"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/neural-argument-generation-augmented-with
|
1805.10254
| null | null |
Neural Argument Generation Augmented with Externally Retrieved Evidence
|
High quality arguments are essential elements for human reasoning and
decision-making processes. However, effective argument construction is a
challenging task for both human and machines. In this work, we study a novel
task on automatically generating arguments of a different stance for a given
statement. We propose an encoder-decoder style neural network-based argument
generation model enriched with externally retrieved evidence from Wikipedia.
Our model first generates a set of talking point phrases as intermediate
representation, followed by a separate decoder producing the final argument
based on both input and the keyphrases. Experiments on a large-scale dataset
collected from Reddit show that our model constructs arguments with more
topic-relevant content than a popular sequence-to-sequence generation model
according to both automatic evaluation and human assessments.
| null |
http://arxiv.org/abs/1805.10254v1
|
http://arxiv.org/pdf/1805.10254v1.pdf
|
ACL 2018 7
|
[
"Xinyu Hua",
"Lu Wang"
] |
[
"Decision Making",
"Decoder"
] | 2018-05-25T00:00:00 |
https://aclanthology.org/P18-1021
|
https://aclanthology.org/P18-1021.pdf
|
neural-argument-generation-augmented-with-1
| null |
[] |
https://paperswithcode.com/paper/intrinsic-image-transformation-via-scale
|
1805.10253
| null | null |
Intrinsic Image Transformation via Scale Space Decomposition
|
We introduce a new network structure for decomposing an image into its
intrinsic albedo and shading. We treat this as an image-to-image transformation
problem and explore the scale space of the input and output. By expanding the
output images (albedo and shading) into their Laplacian pyramid components, we
develop a multi-channel network structure that learns the image-to-image
transformation function in successive frequency bands in parallel, within each
channel is a fully convolutional neural network with skip connections. This
network structure is general and extensible, and has demonstrated excellent
performance on the intrinsic image decomposition problem. We evaluate the
network on two benchmark datasets: the MPI-Sintel dataset and the MIT Intrinsic
Images dataset. Both quantitative and qualitative results show our model
delivers a clear progression over state-of-the-art.
| null |
http://arxiv.org/abs/1805.10253v1
|
http://arxiv.org/pdf/1805.10253v1.pdf
|
CVPR 2018 6
|
[
"Lechao Cheng",
"Chengyi Zhang",
"Zicheng Liao"
] |
[
"Intrinsic Image Decomposition"
] | 2018-05-25T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Cheng_Intrinsic_Image_Transformation_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Cheng_Intrinsic_Image_Transformation_CVPR_2018_paper.pdf
|
intrinsic-image-transformation-via-scale-1
| null |
[] |
https://paperswithcode.com/paper/how-much-restricted-isometry-is-needed-in
|
1805.10251
| null | null |
How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?
|
When the linear measurements of an instance of low-rank matrix recovery
satisfy a restricted isometry property (RIP)---i.e. they are approximately
norm-preserving---the problem is known to contain no spurious local minima, so
exact recovery is guaranteed. In this paper, we show that moderate RIP is not
enough to eliminate spurious local minima, so existing results can only hold
for near-perfect RIP. In fact, counterexamples are ubiquitous: we prove that
every x is the spurious local minimum of a rank-1 instance of matrix recovery
that satisfies RIP. One specific counterexample has RIP constant $\delta=1/2$,
but causes randomly initialized stochastic gradient descent (SGD) to fail 12%
of the time. SGD is frequently able to avoid and escape spurious local minima,
but this empirical result shows that it can occasionally be defeated by their
existence. Hence, while exact recovery guarantees will likely require a proof
of no spurious local minima, arguments based solely on norm preservation will
only be applicable to a narrow set of nearly-isotropic instances.
| null |
http://arxiv.org/abs/1805.10251v2
|
http://arxiv.org/pdf/1805.10251v2.pdf
|
NeurIPS 2018 12
|
[
"Richard Y. Zhang",
"Cédric Josz",
"Somayeh Sojoudi",
"Javad Lavaei"
] |
[] | 2018-05-25T00:00:00 |
http://papers.nips.cc/paper/7802-how-much-restricted-isometry-is-needed-in-nonconvex-matrix-recovery
|
http://papers.nips.cc/paper/7802-how-much-restricted-isometry-is-needed-in-nonconvex-matrix-recovery.pdf
|
how-much-restricted-isometry-is-needed-in-1
| null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using all available data. That is for weights $w$ and a loss function $L$ we have:\r\n\r\n$$ w\\_{t+1} = w\\_{t} - \\eta\\hat{\\nabla}\\_{w}{L(w\\_{t})} $$\r\n\r\nWhere $\\eta$ is a learning rate. SGD reduces redundancy compared to batch gradient descent - which recomputes gradients for similar examples before each parameter update - so it is usually much faster.\r\n\r\n(Image Source: [here](http://rasbt.github.io/mlxtend/user_guide/general_concepts/gradient-optimization/))",
"full_name": "Stochastic Gradient Descent",
"introduced_year": 1951,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "SGD",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/same-different-problems-strain-convolutional
|
1802.03390
| null | null |
Same-different problems strain convolutional neural networks
|
The robust and efficient recognition of visual relations in images is a
hallmark of biological vision. We argue that, despite recent progress in visual
recognition, modern machine vision algorithms are severely limited in their
ability to learn visual relations. Through controlled experiments, we
demonstrate that visual-relation problems strain convolutional neural networks
(CNNs). The networks eventually break altogether when rote memorization becomes
impossible, as when intra-class variability exceeds network capacity. Motivated
by the comparable success of biological vision, we argue that feedback
mechanisms including attention and perceptual grouping may be the key
computational components underlying abstract visual reasoning.\
| null |
http://arxiv.org/abs/1802.03390v3
|
http://arxiv.org/pdf/1802.03390v3.pdf
| null |
[
"Matthew Ricci",
"Junkyung Kim",
"Thomas Serre"
] |
[
"Memorization",
"Visual Reasoning"
] | 2018-02-09T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/exact-camera-location-recovery-by-least
|
1709.09683
| null | null |
Exact Camera Location Recovery by Least Unsquared Deviations
|
We establish exact recovery for the Least Unsquared Deviations (LUD)
algorithm of Ozyesil and Singer. More precisely, we show that for sufficiently
many cameras with given corrupted pairwise directions, where both camera
locations and pairwise directions are generated by a special probabilistic
model, the LUD algorithm exactly recovers the camera locations with high
probability. A similar exact recovery guarantee was established for the
ShapeFit algorithm by Hand, Lee and Voroninski, but with typically less
corruption.
| null |
http://arxiv.org/abs/1709.09683v4
|
http://arxiv.org/pdf/1709.09683v4.pdf
| null |
[
"Gilad Lerman",
"Yunpeng Shi",
"Teng Zhang"
] |
[] | 2017-09-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/graph-oracle-models-lower-bounds-and-gaps-for
|
1805.10222
| null | null |
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
|
We suggest a general oracle-based framework that captures different parallel
stochastic optimization settings described by a dependency graph, and derive
generic lower bounds in terms of this graph. We then use the framework and
derive lower bounds for several specific parallel optimization settings,
including delayed updates and parallel processing with intermittent
communication. We highlight gaps between lower and upper bounds on the oracle
complexity, and cases where the "natural" algorithms are not known to be
optimal.
| null |
http://arxiv.org/abs/1805.10222v3
|
http://arxiv.org/pdf/1805.10222v3.pdf
|
NeurIPS 2018 12
|
[
"Blake Woodworth",
"Jialei Wang",
"Adam Smith",
"Brendan Mcmahan",
"Nathan Srebro"
] |
[
"Stochastic Optimization"
] | 2018-05-25T00:00:00 |
http://papers.nips.cc/paper/8069-graph-oracle-models-lower-bounds-and-gaps-for-parallel-stochastic-optimization
|
http://papers.nips.cc/paper/8069-graph-oracle-models-lower-bounds-and-gaps-for-parallel-stochastic-optimization.pdf
|
graph-oracle-models-lower-bounds-and-gaps-for-1
| null |
[] |
https://paperswithcode.com/paper/generating-thematic-chinese-poetry-using
|
1711.07632
| null | null |
Generating Thematic Chinese Poetry using Conditional Variational Autoencoders with Hybrid Decoders
|
Computer poetry generation is our first step towards computer writing. Writing must have a theme. The current approaches of using sequence-to-sequence models with attention often produce non-thematic poems. We present a novel conditional variational autoencoder with a hybrid decoder adding the deconvolutional neural networks to the general recurrent neural networks to fully learn topic information via latent variables. This approach significantly improves the relevance of the generated poems by representing each line of the poem not only in a context-sensitive manner but also in a holistic way that is highly related to the given keyword and the learned topic. A proposed augmented word2vec model further improves the rhythm and symmetry. Tests show that the generated poems by our approach are mostly satisfying with regulated rules and consistent themes, and 73.42% of them receive an Overall score no less than 3 (the highest score is 5).
| null |
https://arxiv.org/abs/1711.07632v4
|
https://arxiv.org/pdf/1711.07632v4.pdf
| null |
[
"Xiaopeng Yang",
"Xiaowen Lin",
"Shunda Suo",
"Ming Li"
] |
[
"Decoder",
"Rhythm"
] | 2017-11-21T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Solana transaction not confirmed, your Solana wallet not showing balance, or you're trying to recover a lost Solana wallet, knowing where to get help is essential. That’s why the Solana customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Solana Customer Support Number +1-833-534-1729\r\nSolana operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Solana Transaction Not Confirmed\r\nOne of the most common concerns is when a Solana transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Solana Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Solana wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Solana Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Solana wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Solana Deposit Not Received\r\nIf someone has sent you Solana but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Solana deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Solana Transaction Stuck or Pending\r\nSometimes your Solana transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Solana Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Solana wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Solana Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Solana tech.\r\n\r\n24/7 Availability: Solana doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Solana Support and Wallet Issues\r\nQ1: Can Solana support help me recover stolen BTC?\r\nA: While Solana transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Solana transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Solana’s official number (Solana is decentralized), it connects you to trained professionals experienced in resolving all major Solana issues.\r\n\r\nFinal Thoughts\r\nSolana is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Solana transaction not confirmed, your Solana wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Solana customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Solana Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Solana Customer Service Number +1-833-534-1729",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
}
] |
https://paperswithcode.com/paper/multiview-learning-of-weighted-majority-vote
|
1805.10212
| null | null |
Multiview Learning of Weighted Majority Vote by Bregman Divergence Minimization
|
We tackle the issue of classifier combinations when observations have
multiple views. Our method jointly learns view-specific weighted majority vote
classifiers (i.e. for each view) over a set of base voters, and a second
weighted majority vote classifier over the set of these view-specific weighted
majority vote classifiers. We show that the empirical risk minimization of the
final majority vote given a multiview training set can be cast as the
minimization of Bregman divergences. This allows us to derive a parallel-update
optimization algorithm for learning our multiview model. We empirically study
our algorithm with a particular focus on the impact of the training set size on
the multiview learning results. The experiments show that our approach is able
to overcome the lack of labeled information.
|
We tackle the issue of classifier combinations when observations have multiple views.
|
http://arxiv.org/abs/1805.10212v1
|
http://arxiv.org/pdf/1805.10212v1.pdf
| null |
[
"Anil Goyal",
"Emilie Morvant",
"Massih-Reza Amini"
] |
[
"Document Classification",
"Multilingual text classification",
"Multiview Learning",
"Text Classification"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-quasi-bayesian-perspective-to-online
|
1602.00522
| null | null |
A Quasi-Bayesian Perspective to Online Clustering
|
When faced with high frequency streams of data, clustering raises theoretical
and algorithmic pitfalls. We introduce a new and adaptive online clustering
algorithm relying on a quasi-Bayesian approach, with a dynamic (i.e.,
time-dependent) estimation of the (unknown and changing) number of clusters. We
prove that our approach is supported by minimax regret bounds. We also provide
an RJMCMC-flavored implementation (called PACBO, see
https://cran.r-project.org/web/packages/PACBO/index.html) for which we give a
convergence guarantee. Finally, numerical experiments illustrate the potential
of our procedure.
| null |
http://arxiv.org/abs/1602.00522v3
|
http://arxiv.org/pdf/1602.00522v3.pdf
| null |
[
"Le Li",
"Benjamin Guedj",
"Sébastien Loustau"
] |
[
"Clustering",
"Online Clustering"
] | 2016-02-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/coreclust-a-new-package-for-a-robust-and
|
1805.10211
| null | null |
COREclust: a new package for a robust and scalable analysis of complex data
|
In this paper, we present a new R package COREclust dedicated to the
detection of representative variables in high dimensional spaces with a
potentially limited number of observations. Variable sets detection is based on
an original graph clustering strategy denoted CORE-clustering algorithm that
detects CORE-clusters, i.e. variable sets having a user defined size range and
in which each variable is very similar to at least another variable.
Representative variables are then robustely estimate as the CORE-cluster
centers. This strategy is entirely coded in C++ and wrapped by R using the Rcpp
package. A particular effort has been dedicated to keep its algorithmic cost
reasonable so that it can be used on large datasets. After motivating our work,
we will explain the CORE-clustering algorithm as well as a greedy extension of
this algorithm. We will then present how to use it and results obtained on
synthetic and real data.
| null |
http://arxiv.org/abs/1805.10211v1
|
http://arxiv.org/pdf/1805.10211v1.pdf
| null |
[
"Camille Champion",
"Anne-Claire Brunet",
"Jean-Michel Loubes",
"Laurent Risser"
] |
[
"Clustering",
"Graph Clustering"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/psychophysics-gestalts-and-games
|
1805.10210
| null | null |
Psychophysics, Gestalts and Games
|
Many psychophysical studies are dedicated to the evaluation of the human
gestalt detection on dot or Gabor patterns, and to model its dependence on the
pattern and background parameters. Nevertheless, even for these constrained
percepts, psychophysics have not yet reached the challenging prediction stage,
where human detection would be quantitatively predicted by a (generic) model.
On the other hand, Computer Vision has attempted at defining automatic
detection thresholds. This chapter sketches a procedure to confront these two
methodologies inspired in gestaltism. Using a computational quantitative
version of the non-accidentalness principle, we raise the possibility that the
psychophysical and the (older) gestaltist setups, both applicable on dot or
Gabor patterns, find a useful complement in a Turing test. In our perceptual
Turing test, human performance is compared by the scientist to the detection
result given by a computer. This confrontation permits to revive the abandoned
method of gestaltic games. We sketch the elaboration of such a game, where the
subjects of the experiment are confronted to an alignment detection algorithm,
and are invited to draw examples that will fool it. We show that in that way a
more precise definition of the alignment gestalt and of its computational
formulation seems to emerge. Detection algorithms might also be relevant to
more classic psychophysical setups, where they can again play the role of a
Turing test. To a visual experiment where subjects were invited to detect
alignments in Gabor patterns, we associated a single function measuring the
alignment detectability in the form of a number of false alarms (NFA). The
first results indicate that the values of the NFA, as a function of all
simulation parameters, are highly correlated to the human detection. This fact,
that we intend to support by further experiments , might end up confirming that
human alignment detection is the result of a single mechanism.
| null |
http://arxiv.org/abs/1805.10210v1
|
http://arxiv.org/pdf/1805.10210v1.pdf
| null |
[
"José Lezama",
"Samy Blusseau",
"Jean-Michel Morel",
"Gregory Randall",
"Rafael Grompone von Gioi"
] |
[
"Human Detection"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-the-estimation-of-entropy-in-the-fastica
|
1805.10206
| null | null |
On the Estimation of Entropy in the FastICA Algorithm
|
The fastICA method is a popular dimension reduction technique used to reveal patterns in data. Here we show both theoretically and in practice that the approximations used in fastICA can result in patterns not being successfully recognised. We demonstrate this problem using a two-dimensional example where a clear structure is immediately visible to the naked eye, but where the projection chosen by fastICA fails to reveal this structure. This implies that care is needed when applying fastICA. We discuss how the problem arises and how it is intrinsically connected to the approximations that form the basis of the computational efficiency of fastICA.
|
The fastICA method is a popular dimension reduction technique used to reveal patterns in data.
|
https://arxiv.org/abs/1805.10206v5
|
https://arxiv.org/pdf/1805.10206v5.pdf
| null |
[
"Elena Issoglio",
"Paul Smith",
"Jochen Voss"
] |
[
"Computational Efficiency",
"Dimensionality Reduction"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multimodal-sentiment-analysis-to-explore-the
|
1805.10205
| null |
BJ6anzb0Z
|
Multimodal Sentiment Analysis To Explore the Structure of Emotions
|
We propose a novel approach to multimodal sentiment analysis using deep
neural networks combining visual analysis and natural language processing. Our
goal is different than the standard sentiment analysis goal of predicting
whether a sentence expresses positive or negative sentiment; instead, we aim to
infer the latent emotional state of the user. Thus, we focus on predicting the
emotion word tags attached by users to their Tumblr posts, treating these as
"self-reported emotions." We demonstrate that our multimodal model combining
both text and image features outperforms separate models based solely on either
images or text. Our model's results are interpretable, automatically yielding
sensible word lists associated with emotions. We explore the structure of
emotions implied by our model and compare it to what has been posited in the
psychology literature, and validate our model on a set of images that have been
used in psychology studies. Finally, our work also provides a useful tool for
the growing academic study of images - both photographs and memes - on social
networks.
|
We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing.
|
http://arxiv.org/abs/1805.10205v1
|
http://arxiv.org/pdf/1805.10205v1.pdf
|
ICLR 2018 1
|
[
"Anthony Hu",
"Seth Flaxman"
] |
[
"Multimodal Sentiment Analysis",
"Sentence",
"Sentiment Analysis"
] | 2018-05-25T00:00:00 |
https://openreview.net/forum?id=BJ6anzb0Z
|
https://openreview.net/pdf?id=BJ6anzb0Z
|
multimodal-sentiment-analysis-to-explore-the-1
| null |
[] |
https://paperswithcode.com/paper/adversarial-examples-from-computational
|
1805.10204
| null | null |
Adversarial examples from computational constraints
|
Why are classifiers in high dimension vulnerable to "adversarial"
perturbations? We show that it is likely not due to information theoretic
limitations, but rather it could be due to computational constraints.
First we prove that, for a broad set of classification tasks, the mere
existence of a robust classifier implies that it can be found by a possibly
exponential-time algorithm with relatively few training examples. Then we give
a particular classification task where learning a robust classifier is
computationally intractable. More precisely we construct a binary
classification task in high dimensional space which is (i) information
theoretically easy to learn robustly for large perturbations, (ii) efficiently
learnable (non-robustly) by a simple linear separator, (iii) yet is not
efficiently robustly learnable, even for small perturbations, by any algorithm
in the statistical query (SQ) model. This example gives an exponential
separation between classical learning and robust learning in the statistical
query model. It suggests that adversarial examples may be an unavoidable
byproduct of computational limitations of learning algorithms.
| null |
http://arxiv.org/abs/1805.10204v1
|
http://arxiv.org/pdf/1805.10204v1.pdf
| null |
[
"Sébastien Bubeck",
"Eric Price",
"Ilya Razenshteyn"
] |
[
"Binary Classification",
"Classification",
"General Classification"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/qunatification-of-metabolites-in-mr
|
1805.10201
| null | null |
Qunatification of Metabolites in MR Spectroscopic Imaging using Machine Learning
|
Magnetic Resonance Spectroscopic Imaging (MRSI) is a clinical imaging
modality for measuring tissue metabolite levels in-vivo. An accurate estimation
of spectral parameters allows for better assessment of spectral quality and
metabolite concentration levels. The current gold standard quantification
method is the LCModel - a commercial fitting tool. However, this fails for
spectra having poor signal-to-noise ratio (SNR) or a large number of artifacts.
This paper introduces a framework based on random forest regression for
accurate estimation of the output parameters of a model based analysis of MR
spectroscopy data. The goal of our proposed framework is to learn the spectral
features from a training set comprising of different variations of both
simulated and in-vivo brain spectra and then use this learning for the
subsequent metabolite quantification. Experiments involve training and testing
on simulated and in-vivo human brain spectra. We estimate parameters such as
concentration of metabolites and compare our results with that from the
LCModel.
| null |
http://arxiv.org/abs/1805.10201v1
|
http://arxiv.org/pdf/1805.10201v1.pdf
| null |
[
"Dhritiman Das",
"Eduardo Coello",
"Rolf F Schulte",
"Bjoern H. Menze"
] |
[
"BIG-bench Machine Learning"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/maximizing-acquisition-functions-for-bayesian
|
1805.10196
| null | null |
Maximizing acquisition functions for Bayesian optimization
|
Bayesian optimization is a sample-efficient approach to global optimization
that relies on theoretically motivated value heuristics (acquisition functions)
to guide its search process. Fully maximizing acquisition functions produces
the Bayes' decision rule, but this ideal is difficult to achieve since these
functions are frequently non-trivial to optimize. This statement is especially
true when evaluating queries in parallel, where acquisition functions are
routinely non-convex, high-dimensional, and intractable. We first show that
acquisition functions estimated via Monte Carlo integration are consistently
amenable to gradient-based optimization. Subsequently, we identify a common
family of acquisition functions, including EI and UCB, whose properties not
only facilitate but justify use of greedy approaches for their maximization.
|
Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process.
|
http://arxiv.org/abs/1805.10196v2
|
http://arxiv.org/pdf/1805.10196v2.pdf
|
NeurIPS 2018 12
|
[
"James T. Wilson",
"Frank Hutter",
"Marc Peter Deisenroth"
] |
[
"Bayesian Optimization",
"global-optimization"
] | 2018-05-25T00:00:00 |
http://papers.nips.cc/paper/8194-maximizing-acquisition-functions-for-bayesian-optimization
|
http://papers.nips.cc/paper/8194-maximizing-acquisition-functions-for-bayesian-optimization.pdf
|
maximizing-acquisition-functions-for-bayesian-1
| null |
[] |
https://paperswithcode.com/paper/personalized-influence-estimation-technique
|
1805.10940
| null | null |
Personalized Influence Estimation Technique
|
Customer Satisfaction is the most important factors in the industry
irrespective of domain. Key Driver Analysis is a common practice in data
science to help the business to evaluate the same. Understanding key features,
which influence the outcome or dependent feature, is highly important in
statistical model building. This helps to eliminate not so important factors
from the model to minimize noise coming from the features, which does not
contribute significantly enough to explain the behavior of the dependent
feature, which we want to predict. Personalized Influence Estimation is a
technique introduced in this paper, which can estimate key factor influence for
individual observations, which contribute most for each observations behavior
pattern based on the dependent class or estimate. Observations can come from
multiple business problem i.e. customers related to satisfaction study,
customer related to Fraud Detection, network devices for Fault detection etc.
It is highly important to understand the cause of issue at each observation
level to take appropriate Individualized action at customer level or device
level etc. This technique is based on joint behavior of the feature dimension
for the specific observation, and relative importance of the feature to
estimate impact. The technique mentioned in this paper is aimed to help
organizations to understand each respondents or observations individual key
contributing factor of Influence. Result of the experiment is really
encouraging and able to justify key reasons for churn for majority of the
sample appropriately
| null |
http://arxiv.org/abs/1805.10940v1
|
http://arxiv.org/pdf/1805.10940v1.pdf
| null |
[
"Kumarjit Pathak",
"Jitin Kapila",
"Aasheesh Barvey"
] |
[
"Fault Detection",
"Fraud Detection"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/overcoming-the-vanishing-gradient-problem-in
|
1801.06105
| null |
Hyp3i2xRb
|
Overcoming the vanishing gradient problem in plain recurrent networks
|
Plain recurrent networks greatly suffer from the vanishing gradient problem while Gated Neural Networks (GNNs) such as Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU) deliver promising results in many sequence learning tasks through sophisticated network designs. This paper shows how we can address this problem in a plain recurrent network by analyzing the gating mechanisms in GNNs. We propose a novel network called the Recurrent Identity Network (RIN) which allows a plain recurrent network to overcome the vanishing gradient problem while training very deep models without the use of gates. We compare this model with IRNNs and LSTMs on multiple sequence modeling benchmarks. The RINs demonstrate competitive performance and converge faster in all tasks. Notably, small RIN models produce 12%--67% higher accuracy on the Sequential and Permuted MNIST datasets and reach state-of-the-art performance on the bAbI question answering dataset.
| null |
https://arxiv.org/abs/1801.06105v3
|
https://arxiv.org/pdf/1801.06105v3.pdf
|
ICLR 2018 1
|
[
"Yuhuang Hu",
"Adrian Huber",
"Jithendar Anumula",
"Shih-Chii Liu"
] |
[
"Permuted-MNIST",
"Question Answering"
] | 2018-01-18T00:00:00 |
https://openreview.net/forum?id=Hyp3i2xRb
|
https://openreview.net/pdf?id=Hyp3i2xRb
|
overcoming-the-vanishing-gradient-problem-in-1
| null |
[] |
https://paperswithcode.com/paper/snips-voice-platform-an-embedded-spoken
|
1805.10190
| null | null |
Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces
|
This paper presents the machine learning architecture of the Snips Voice
Platform, a software solution to perform Spoken Language Understanding on
microprocessors typical of IoT devices. The embedded inference is fast and
accurate while enforcing privacy by design, as no personal user data is ever
collected. Focusing on Automatic Speech Recognition and Natural Language
Understanding, we detail our approach to training high-performance Machine
Learning models that are small enough to run in real-time on small devices.
Additionally, we describe a data generation procedure that provides sufficient,
high-quality training data without compromising user privacy.
|
This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices.
|
http://arxiv.org/abs/1805.10190v3
|
http://arxiv.org/pdf/1805.10190v3.pdf
| null |
[
"Alice Coucke",
"Alaa Saade",
"Adrien Ball",
"Théodore Bluche",
"Alexandre Caulier",
"David Leroy",
"Clément Doumouro",
"Thibault Gisselbrecht",
"Francesco Caltagirone",
"Thibaut Lavril",
"Maël Primet",
"Joseph Dureau"
] |
[
"Automatic Speech Recognition",
"Automatic Speech Recognition (ASR)",
"BIG-bench Machine Learning",
"Natural Language Understanding",
"Speech Recognition",
"Spoken Language Understanding"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/recursive-neural-network-based-preordering
|
1805.10187
| null | null |
Recursive Neural Network Based Preordering for English-to-Japanese Machine Translation
|
The word order between source and target languages significantly influences
the translation quality in machine translation. Preordering can effectively
address this problem. Previous preordering methods require a manual feature
design, making language dependent design costly. In this paper, we propose a
preordering method with a recursive neural network that learns features from
raw inputs. Experiments show that the proposed method achieves comparable gain
in translation quality to the state-of-the-art method but without a manual
feature design.
| null |
http://arxiv.org/abs/1805.10187v1
|
http://arxiv.org/pdf/1805.10187v1.pdf
|
ACL 2018 7
|
[
"Yuki Kawara",
"Chenhui Chu",
"Yuki Arase"
] |
[
"Machine Translation",
"Translation"
] | 2018-05-25T00:00:00 |
https://aclanthology.org/P18-3004
|
https://aclanthology.org/P18-3004.pdf
|
recursive-neural-network-based-preordering-1
| null |
[] |
https://paperswithcode.com/paper/a-generative-model-for-inverse-design-of
|
1805.10181
| null | null |
A Generative Model for Inverse Design of Metamaterials
|
The advent of two-dimensional metamaterials in recent years has ushered in a
revolutionary means to manipulate the behavior of light on the nanoscale. The
effective parameters of these architected materials render unprecedented
control over the optical properties of light, thereby eliciting previously
unattainable applications in flat lenses, holographic imaging, and emission
control among others. The design of such structures, to date, has relied on the
expertise of an optical scientist to guide a progression of electromagnetic
simulations that iteratively solve Maxwell's equations until a locally
optimized solution can be attained. In this work, we identify a solution to
circumvent this intuition-guided design by means of a deep learning
architecture. When fed an input set of optical spectra, the constructed
generative network assimilates a candidate pattern from a user-defined dataset
of geometric structures in order to match the input spectra. The generated
metamaterial patterns demonstrate high fidelity, yielding equivalent optical
spectra at an average accuracy of about 0.9. This approach reveals an
opportunity to expedite the discovery and design of metasurfaces for tailored
optical responses in a systematic, inverse-design manner.
| null |
http://arxiv.org/abs/1805.10181v1
|
http://arxiv.org/pdf/1805.10181v1.pdf
| null |
[
"Zhaocheng Liu",
"Dayu Zhu",
"Sean P. Rodrigues",
"Kyu-Tae Lee",
"Wenshan Cai"
] |
[] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/pyramid-attention-network-for-semantic
|
1805.10180
| null | null |
Pyramid Attention Network for Semantic Segmentation
|
A Pyramid Attention Network(PAN) is proposed to exploit the impact of global
contextual information in semantic segmentation. Different from most existing
works, we combine attention mechanism and spatial pyramid to extract precise
dense features for pixel labeling instead of complicated dilated convolution
and artificially designed decoder networks. Specifically, we introduce a
Feature Pyramid Attention module to perform spatial pyramid attention structure
on high-level output and combining global pooling to learn a better feature
representation, and a Global Attention Upsample module on each decoder layer to
provide global context as a guidance of low-level features to select category
localization details. The proposed approach achieves state-of-the-art
performance on PASCAL VOC 2012 and Cityscapes benchmarks with a new record of
mIoU accuracy 84.0% on PASCAL VOC 2012, while training without COCO dataset.
| null |
http://arxiv.org/abs/1805.10180v3
|
http://arxiv.org/pdf/1805.10180v3.pdf
| null |
[
"Hanchao Li",
"Pengfei Xiong",
"Jie An",
"Lingxue Wang"
] |
[
"Decoder",
"Segmentation",
"Semantic Segmentation",
"Thermal Image Segmentation"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-univariate-bound-of-area-under-roc
|
1804.05981
| null | null |
A Univariate Bound of Area Under ROC
|
Area under ROC (AUC) is an important metric for binary classification and
bipartite ranking problems. However, it is difficult to directly optimizing AUC
as a learning objective, so most existing algorithms are based on optimizing a
surrogate loss to AUC. One significant drawback of these surrogate losses is
that they require pairwise comparisons among training data, which leads to slow
running time and increasing local storage for online learning. In this work, we
describe a new surrogate loss based on a reformulation of the AUC risk, which
does not require pairwise comparison but rankings of the predictions. We
further show that the ranking operation can be avoided, and the learning
objective obtained based on this surrogate enjoys linear complexity in time and
storage. We perform experiments to demonstrate the effectiveness of the online
and batch algorithms for AUC optimization based on the proposed surrogate loss.
| null |
http://arxiv.org/abs/1804.05981v2
|
http://arxiv.org/pdf/1804.05981v2.pdf
| null |
[
"Siwei Lyu",
"Yiming Ying"
] |
[
"Binary Classification"
] | 2018-04-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/f-cnntextx-a-toolflow-for-mapping-multiple
|
1805.10174
| null | null |
f-CNN$^{\text{x}}$: A Toolflow for Mapping Multi-CNN Applications on FPGAs
|
The predictive power of Convolutional Neural Networks (CNNs) has been an integral factor for emerging latency-sensitive applications, such as autonomous drones and vehicles. Such systems employ multiple CNNs, each one trained for a particular task. The efficient mapping of multiple CNNs on a single FPGA device is a challenging task as the allocation of compute resources and external memory bandwidth needs to be optimised at design time. This paper proposes f-CNN$^{\text{x}}$, an automated toolflow for the optimised mapping of multiple CNNs on FPGAs, comprising a novel multi-CNN hardware architecture together with an automated design space exploration method that considers the user-specified performance requirements for each model to allocate compute resources and generate a synthesisable accelerator. Moreover, f-CNN$^{\text{x}}$ employs a novel scheduling algorithm that alleviates the limitations of the memory bandwidth contention between CNNs and sustains the high utilisation of the architecture. Experimental evaluation shows that f-CNN$^{\text{x}}$'s designs outperform contention-unaware FPGA mappings by up to 50% and deliver up to 6.8x higher performance-per-Watt over highly optimised GPU designs for multi-CNN systems.
| null |
https://arxiv.org/abs/1805.10174v2
|
https://arxiv.org/pdf/1805.10174v2.pdf
| null |
[
"Stylianos I. Venieris",
"Christos-Savvas Bouganis"
] |
[
"GPU",
"Scheduling"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-lifelong-learning-approach-to-brain-mr
|
1805.10170
| null | null |
A Lifelong Learning Approach to Brain MR Segmentation Across Scanners and Protocols
|
Convolutional neural networks (CNNs) have shown promising results on several
segmentation tasks in magnetic resonance (MR) images. However, the accuracy of
CNNs may degrade severely when segmenting images acquired with different
scanners and/or protocols as compared to the training data, thus limiting their
practical utility. We address this shortcoming in a lifelong multi-domain
learning setting by treating images acquired with different scanners or
protocols as samples from different, but related domains. Our solution is a
single CNN with shared convolutional filters and domain-specific batch
normalization layers, which can be tuned to new domains with only a few
($\approx$ 4) labelled images. Importantly, this is achieved while retaining
performance on the older domains whose training data may no longer be
available. We evaluate the method for brain structure segmentation in MR
images. Results demonstrate that the proposed method largely closes the gap to
the benchmark, which is training a dedicated CNN for each scanner.
|
We evaluate the method for brain structure segmentation in MR images.
|
http://arxiv.org/abs/1805.10170v1
|
http://arxiv.org/pdf/1805.10170v1.pdf
| null |
[
"Neerav Karani",
"Krishna Chaitanya",
"Christian Baumgartner",
"Ender Konukoglu"
] |
[
"Lifelong learning",
"Segmentation"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/destructiveness-of-lexicographic-parsimony
|
1805.10169
| null | null |
Destructiveness of Lexicographic Parsimony Pressure and Alleviation by a Concatenation Crossover in Genetic Programming
|
For theoretical analyses there are two specifics distinguishing GP from many
other areas of evolutionary computation. First, the variable size
representations, in particular yielding a possible bloat (i.e. the growth of
individuals with redundant parts). Second, the role and realization of
crossover, which is particularly central in GP due to the tree-based
representation. Whereas some theoretical work on GP has studied the effects of
bloat, crossover had a surprisingly little share in this work. We analyze a
simple crossover operator in combination with local search, where a preference
for small solutions minimizes bloat (lexicographic parsimony pressure); the
resulting algorithm is denoted Concatenation Crossover GP. For this purpose
three variants of the well-studied MAJORITY test function with large plateaus
are considered. We show that the Concatenation Crossover GP can efficiently
optimize these test functions, while local search cannot be efficient for all
three variants independent of employing bloat control.
|
We show that the Concatenation Crossover GP can efficiently optimize these test functions, while local search cannot be efficient for all three variants independent of employing bloat control.
|
http://arxiv.org/abs/1805.10169v1
|
http://arxiv.org/pdf/1805.10169v1.pdf
| null |
[
"Timo Kötzing",
"J. A. Gregor Lagodzinski",
"Johannes Lengler",
"Anna Melnichenko"
] |
[] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/futuristic-classification-with-dynamic
|
1805.10168
| null | null |
Futuristic Classification with Dynamic Reference Frame Strategy
|
Classification is one of the widely used analytical techniques in data
science domain across different business to associate a pattern which
contribute to the occurrence of certain event which is predicted with some
likelihood. This Paper address a lacuna of creating some time window before the
prediction actually happen to enable organizations some space to act on the
prediction. There are some really good state of the art machine learning
techniques to optimally identify the possible churners in either customer base
or employee base, similarly for fault prediction too if the prediction does not
come with some buffer time to act on the fault it is very difficult to provide
a seamless experience to the user. New concept of reference frame creation is
introduced to solve this problem in this paper
| null |
http://arxiv.org/abs/1805.10168v1
|
http://arxiv.org/pdf/1805.10168v1.pdf
| null |
[
"Kumarjit Pathak",
"Jitin Kapila",
"Aasheesh Barvey"
] |
[
"Classification",
"General Classification",
"Prediction"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/context-aware-neural-machine-translation
|
1805.10163
| null | null |
Context-Aware Neural Machine Translation Learns Anaphora Resolution
|
Standard machine translation systems process sentences in isolation and hence
ignore extra-sentential information, even though extended context can both
prevent mistakes in ambiguous cases and improve translation coherence. We
introduce a context-aware neural machine translation model designed in such way
that the flow of information from the extended context to the translation model
can be controlled and analyzed. We experiment with an English-Russian subtitles
dataset, and observe that much of what is captured by our model deals with
improving pronoun translation. We measure correspondences between induced
attention distributions and coreference relations and observe that the model
implicitly captures anaphora. It is consistent with gains for sentences where
pronouns need to be gendered in translation. Beside improvements in anaphoric
cases, the model also improves in overall BLEU, both over its context-agnostic
version (+0.7) and over simple concatenation of the context and source
sentences (+0.6).
| null |
http://arxiv.org/abs/1805.10163v1
|
http://arxiv.org/pdf/1805.10163v1.pdf
|
ACL 2018 7
|
[
"Elena Voita",
"Pavel Serdyukov",
"Rico Sennrich",
"Ivan Titov"
] |
[
"Machine Translation",
"Translation"
] | 2018-05-25T00:00:00 |
https://aclanthology.org/P18-1117
|
https://aclanthology.org/P18-1117.pdf
|
context-aware-neural-machine-translation-1
| null |
[] |
https://paperswithcode.com/paper/identifiability-of-kronecker-structured
|
1712.03471
| null | null |
Identifiability of Kronecker-structured Dictionaries for Tensor Data
|
This paper derives sufficient conditions for local recovery of coordinate
dictionaries comprising a Kronecker-structured dictionary that is used for
representing $K$th-order tensor data. Tensor observations are assumed to be
generated from a Kronecker-structured dictionary multiplied by sparse
coefficient tensors that follow the separable sparsity model. This work
provides sufficient conditions on the underlying coordinate dictionaries,
coefficient and noise distributions, and number of samples that guarantee
recovery of the individual coordinate dictionaries up to a specified error, as
a local minimum of the objective function, with high probability. In
particular, the sample complexity to recover $K$ coordinate dictionaries with
dimensions $m_k \times p_k$ up to estimation error $\varepsilon_k$ is shown to
be $\max_{k \in [K]}\mathcal{O}(m_kp_k^3\varepsilon_k^{-2})$.
| null |
http://arxiv.org/abs/1712.03471v3
|
http://arxiv.org/pdf/1712.03471v3.pdf
| null |
[
"Zahra Shakeri",
"Anand D. Sarwate",
"Waheed U. Bajwa"
] |
[] | 2017-12-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/think-visually-question-answering-through
|
1805.11025
| null | null |
Think Visually: Question Answering through Virtual Imagery
|
In this paper, we study the problem of geometric reasoning in the context of
question-answering. We introduce Dynamic Spatial Memory Network (DSMN), a new
deep network architecture designed for answering questions that admit latent
visual representations. DSMN learns to generate and reason over such
representations. Further, we propose two synthetic benchmarks, FloorPlanQA and
ShapeIntersection, to evaluate the geometric reasoning capability of QA
systems. Experimental results validate the effectiveness of our proposed DSMN
for visual thinking tasks.
|
In this paper, we study the problem of geometric reasoning in the context of question-answering.
|
http://arxiv.org/abs/1805.11025v1
|
http://arxiv.org/pdf/1805.11025v1.pdf
|
ACL 2018 7
|
[
"Ankit Goyal",
"Jian Wang",
"Jia Deng"
] |
[
"Question Answering",
"Visual Commonsense Reasoning"
] | 2018-05-25T00:00:00 |
https://aclanthology.org/P18-1242
|
https://aclanthology.org/P18-1242.pdf
|
think-visually-question-answering-through-1
| null |
[
{
"code_snippet_url": "https://github.com/aykutaaykut/Memory-Networks",
"description": "A **Memory Network** provides a memory component that can be read from and written to with the inference capabilities of a neural network model. The motivation is that many neural networks lack a long-term memory component, and their existing memory component encoded by states and weights is too small and not compartmentalized enough to accurately remember facts from the past (RNNs for example, have difficult memorizing and doing tasks like copying). \r\n\r\nA memory network consists of a memory $\\textbf{m}$ (an array of objects indexed by $\\textbf{m}\\_{i}$ and four potentially learned components:\r\n\r\n- Input feature map $I$ - feature representation of the data input.\r\n- Generalization $G$ - updates old memories given the new input.\r\n- Output feature map $O$ - produces new feature map given $I$ and $G$.\r\n- Response $R$ - converts output into the desired response. \r\n\r\nGiven an input $x$ (e.g., an input character, word or sentence depending on the granularity chosen, an image or an audio signal) the flow of the model is as follows:\r\n\r\n1. Convert $x$ to an internal feature representation $I\\left(x\\right)$.\r\n2. Update memories $m\\_{i}$ given the new input: $m\\_{i} = G\\left(m\\_{i}, I\\left(x\\right), m\\right)$, $\\forall{i}$.\r\n3. Compute output features $o$ given the new input and the memory: $o = O\\left(I\\left(x\\right), m\\right)$.\r\n4. Finally, decode output features $o$ to give the final response: $r = R\\left(o\\right)$.\r\n\r\nThis process is applied at both train and test time, if there is a distinction between such phases, that\r\nis, memories are also stored at test time, but the model parameters of $I$, $G$, $O$ and $R$ are not updated. Memory networks cover a wide class of possible implementations. The components $I$, $G$, $O$ and $R$ can potentially use any existing ideas from the machine learning literature.\r\n\r\nImage Source: [Adrian Colyer](https://blog.acolyer.org/2016/03/10/memory-networks/)",
"full_name": "Memory Network",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Working Memory Models** aim to supplement neural networks with a memory module to increase their capability for memorization and allowing them to more easily perform tasks such as retrieving and copying information. Below you can find a continuously updating list of working memory models.",
"name": "Working Memory Models",
"parent": null
},
"name": "Memory Network",
"source_title": "Memory Networks",
"source_url": "http://arxiv.org/abs/1410.3916v11"
}
] |
https://paperswithcode.com/paper/information-propogation-enhanced-neural
|
1709.01766
| null | null |
Information-Propogation-Enhanced Neural Machine Translation by Relation Model
|
Even though sequence-to-sequence neural machine translation (NMT) model have
achieved state-of-art performance in the recent fewer years, but it is widely
concerned that the recurrent neural network (RNN) units are very hard to
capture the long-distance state information, which means RNN can hardly find
the feature with long term dependency as the sequence becomes longer.
Similarly, convolutional neural network (CNN) is introduced into NMT for
speeding recently, however, CNN focus on capturing the local feature of the
sequence; To relieve this issue, we incorporate a relation network into the
standard encoder-decoder framework to enhance information-propogation in neural
network, ensuring that the information of the source sentence can flow into the
decoder adequately. Experiments show that proposed framework outperforms the
statistical MT model and the state-of-art NMT model significantly on two data
sets with different scales.
| null |
http://arxiv.org/abs/1709.01766v3
|
http://arxiv.org/pdf/1709.01766v3.pdf
| null |
[
"Wen Zhang",
"Jiawei Hu",
"Yang Feng",
"Qun Liu"
] |
[
"Decoder",
"Machine Translation",
"NMT",
"Relation",
"Relation Network",
"Sentence",
"Translation"
] | 2017-09-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/refining-source-representations-with-relation
|
1709.03980
| null | null |
Refining Source Representations with Relation Networks for Neural Machine Translation
|
Although neural machine translation (NMT) with the encoder-decoder framework
has achieved great success in recent times, it still suffers from some
drawbacks: RNNs tend to forget old information which is often useful and the
encoder only operates through words without considering word relationship. To
solve these problems, we introduce a relation networks (RN) into NMT to refine
the encoding representations of the source. In our method, the RN first
augments the representation of each source word with its neighbors and reasons
all the possible pairwise relations between them. Then the source
representations and all the relations are fed to the attention module and the
decoder together, keeping the main encoder-decoder architecture unchanged.
Experiments on two Chinese-to-English data sets in different scales both show
that our method can outperform the competitive baselines significantly.
| null |
http://arxiv.org/abs/1709.03980v3
|
http://arxiv.org/pdf/1709.03980v3.pdf
| null |
[
"Wen Zhang",
"Jiawei Hu",
"Yang Feng",
"Qun Liu"
] |
[
"Decoder",
"Machine Translation",
"NMT",
"Relation",
"Translation"
] | 2017-09-12T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/refining-source-representations-with-relation-1
|
1805.11154
| null | null |
Refining Source Representations with Relation Networks for Neural Machine Translation
|
Although neural machine translation with the encoder-decoder framework has
achieved great success recently, it still suffers drawbacks of forgetting
distant information, which is an inherent disadvantage of recurrent neural
network structure, and disregarding relationship between source words during
encoding step. Whereas in practice, the former information and relationship are
often useful in current step. We target on solving these problems and thus
introduce relation networks to learn better representations of the source. The
relation networks are able to facilitate memorization capability of recurrent
neural network via associating source words with each other, this would also
help retain their relationships. Then the source representations and all the
relations are fed into the attention component together while decoding, with
the main encoder-decoder framework unchanged. Experiments on several datasets
show that our method can improve the translation performance significantly over
the conventional encoder-decoder model and even outperform the approach
involving supervised syntactic knowledge.
| null |
http://arxiv.org/abs/1805.11154v2
|
http://arxiv.org/pdf/1805.11154v2.pdf
|
COLING 2018 8
|
[
"Wen Zhang",
"Jiawei Hu",
"Yang Feng",
"Qun Liu"
] |
[
"Decoder",
"Machine Translation",
"Memorization",
"Relation",
"Translation"
] | 2018-05-25T00:00:00 |
https://aclanthology.org/C18-1110
|
https://aclanthology.org/C18-1110.pdf
|
refining-source-representations-with-relation-3
| null |
[] |
https://paperswithcode.com/paper/mapping-images-to-scene-graphs-with
|
1802.05451
| null | null |
Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
|
Machine understanding of complex images is a key goal of artificial
intelligence. One challenge underlying this task is that visual scenes contain
multiple inter-related objects, and that global context plays an important role
in interpreting the scene. A natural modeling framework for capturing such
effects is structured prediction, which optimizes over complex labels, while
modeling within-label interactions. However, it is unclear what principles
should guide the design of a structured prediction model that utilizes the
power of deep learning components. Here we propose a design principle for such
architectures that follows from a natural requirement of permutation
invariance. We prove a necessary and sufficient characterization for
architectures that follow this invariance, and discuss its implication on model
design. Finally, we show that the resulting model achieves new state of the art
results on the Visual Genome scene graph labeling benchmark, outperforming all
recent approaches.
|
Machine understanding of complex images is a key goal of artificial intelligence.
|
http://arxiv.org/abs/1802.05451v4
|
http://arxiv.org/pdf/1802.05451v4.pdf
|
NeurIPS 2018 12
|
[
"Roei Herzig",
"Moshiko Raboh",
"Gal Chechik",
"Jonathan Berant",
"Amir Globerson"
] |
[
"Scene Graph Classification",
"Scene Graph Generation",
"Structured Prediction"
] | 2018-02-15T00:00:00 |
http://papers.nips.cc/paper/7951-mapping-images-to-scene-graphs-with-permutation-invariant-structured-prediction
|
http://papers.nips.cc/paper/7951-mapping-images-to-scene-graphs-with-permutation-invariant-structured-prediction.pdf
|
mapping-images-to-scene-graphs-with-1
| null |
[] |
https://paperswithcode.com/paper/yedda-a-lightweight-collaborative-text-span
|
1711.03759
| null | null |
YEDDA: A Lightweight Collaborative Text Span Annotation Tool
|
In this paper, we introduce \textsc{Yedda}, a lightweight but efficient and
comprehensive open-source tool for text span annotation. \textsc{Yedda}
provides a systematic solution for text span annotation, ranging from
collaborative user annotation to administrator evaluation and analysis. It
overcomes the low efficiency of traditional text annotation tools by annotating
entities through both command line and shortcut keys, which are configurable
with custom labels. \textsc{Yedda} also gives intelligent recommendations by
learning the up-to-date annotated text. An administrator client is developed to
evaluate annotation quality of multiple annotators and generate detailed
comparison report for each annotator pair. Experiments show that the proposed
system can reduce the annotation time by half compared with existing annotation
tools. And the annotation time can be further compressed by 16.47\% through
intelligent recommendation.
|
And the annotation time can be further compressed by 16. 47\% through intelligent recommendation.
|
http://arxiv.org/abs/1711.03759v3
|
http://arxiv.org/pdf/1711.03759v3.pdf
|
ACL 2018 7
|
[
"Jie Yang",
"Yue Zhang",
"Linwei Li",
"Xingxuan Li"
] |
[
"text annotation"
] | 2017-11-10T00:00:00 |
https://aclanthology.org/P18-4006
|
https://aclanthology.org/P18-4006.pdf
|
yedda-a-lightweight-collaborative-text-span-1
| null |
[] |
https://paperswithcode.com/paper/elfi-engine-for-likelihood-free-inference
|
1708.00707
| null | null |
ELFI: Engine for Likelihood-Free Inference
|
Engine for Likelihood-Free Inference (ELFI) is a Python software library for
performing likelihood-free inference (LFI). ELFI provides a convenient syntax
for arranging components in LFI, such as priors, simulators, summaries or
distances, to a network called ELFI graph. The components can be implemented in
a wide variety of languages. The stand-alone ELFI graph can be used with any of
the available inference methods without modifications. A central method
implemented in ELFI is Bayesian Optimization for Likelihood-Free Inference
(BOLFI), which has recently been shown to accelerate likelihood-free inference
up to several orders of magnitude by surrogate-modelling the distance. ELFI
also has an inbuilt support for output data storing for reuse and analysis, and
supports parallelization of computation from multiple cores up to a cluster
environment. ELFI is designed to be extensible and provides interfaces for
widening its functionality. This makes the adding of new inference methods to
ELFI straightforward and automatically compatible with the inbuilt features.
|
The stand-alone ELFI graph can be used with any of the available inference methods without modifications.
|
http://arxiv.org/abs/1708.00707v3
|
http://arxiv.org/pdf/1708.00707v3.pdf
| null |
[
"Jarno Lintusaari",
"Henri Vuollekoski",
"Antti Kangasrääsiö",
"Kusti Skytén",
"Marko Järvenpää",
"Pekka Marttinen",
"Michael U. Gutmann",
"Aki Vehtari",
"Jukka Corander",
"Samuel Kaski"
] |
[
"Bayesian Optimization"
] | 2017-08-02T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/pooling-is-neither-necessary-nor-sufficient
|
1804.04438
| null |
HJeuOiRqKQ
|
Pooling is neither necessary nor sufficient for appropriate deformation stability in CNNs
|
Many of our core assumptions about how neural networks operate remain
empirically untested. One common assumption is that convolutional neural
networks need to be stable to small translations and deformations to solve
image recognition tasks. For many years, this stability was baked into CNN
architectures by incorporating interleaved pooling layers. Recently, however,
interleaved pooling has largely been abandoned. This raises a number of
questions: Are our intuitions about deformation stability right at all? Is it
important? Is pooling necessary for deformation invariance? If not, how is
deformation invariance achieved in its absence? In this work, we rigorously
test these questions, and find that deformation stability in convolutional
networks is more nuanced than it first appears: (1) Deformation invariance is
not a binary property, but rather that different tasks require different
degrees of deformation stability at different layers. (2) Deformation stability
is not a fixed property of a network and is heavily adjusted over the course of
training, largely through the smoothness of the convolutional filters. (3)
Interleaved pooling layers are neither necessary nor sufficient for achieving
the optimal form of deformation stability for natural image classification. (4)
Pooling confers too much deformation stability for image classification at
initialization, and during training, networks have to learn to counteract this
inductive bias. Together, these findings provide new insights into the role of
interleaved pooling and deformation invariance in CNNs, and demonstrate the
importance of rigorous empirical testing of even our most basic assumptions
about the working of neural networks.
| null |
http://arxiv.org/abs/1804.04438v2
|
http://arxiv.org/pdf/1804.04438v2.pdf
|
ICLR 2019 5
|
[
"Avraham Ruderman",
"Neil C. Rabinowitz",
"Ari S. Morcos",
"Daniel Zoran"
] |
[
"General Classification",
"image-classification",
"Image Classification",
"Inductive Bias"
] | 2018-04-12T00:00:00 |
https://openreview.net/forum?id=HJeuOiRqKQ
|
https://openreview.net/pdf?id=HJeuOiRqKQ
|
pooling-is-neither-necessary-nor-sufficient-1
| null |
[] |
https://paperswithcode.com/paper/function-estimation-via-reconstruction
|
1805.10122
| null | null |
The Reconstruction Approach: From Interpolation to Regression
|
This paper introduces an interpolation-based method, called the reconstruction approach, for nonparametric regression. Based on the fact that interpolation usually has negligible errors compared to statistical estimation, the reconstruction approach uses an interpolator to parameterize the regression function with its values at finite knots, and then estimates these values by (regularized) least squares. Some popular methods including kernel ridge regression can be viewed as its special cases. It is shown that, the reconstruction idea not only provides different angles to look into existing methods, but also produces new effective experimental design and estimation methods for nonparametric models. In particular, for some methods of complexity O(n3), where n is the sample size, this approach provides effective surrogates with much less computational burden. This point makes it very suitable for large datasets.
| null |
https://arxiv.org/abs/1805.10122v3
|
https://arxiv.org/pdf/1805.10122v3.pdf
| null |
[
"Shifeng Xiong"
] |
[
"Experimental Design",
"regression"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/unibuckernel-reloaded-first-place-in-arabic
|
1805.04876
| null | null |
UnibucKernel Reloaded: First Place in Arabic Dialect Identification for the Second Year in a Row
|
We present a machine learning approach that ranked on the first place in the
Arabic Dialect Identification (ADI) Closed Shared Tasks of the 2018 VarDial
Evaluation Campaign. The proposed approach combines several kernels using
multiple kernel learning. While most of our kernels are based on character
p-grams (also known as n-grams) extracted from speech or phonetic transcripts,
we also use a kernel based on dialectal embeddings generated from audio
recordings by the organizers. In the learning stage, we independently employ
Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR).
Preliminary experiments indicate that KRR provides better classification
results. Our approach is shallow and simple, but the empirical results obtained
in the 2018 ADI Closed Shared Task prove that it achieves the best performance.
Furthermore, our top macro-F1 score (58.92%) is significantly better than the
second best score (57.59%) in the 2018 ADI Shared Task, according to the
statistical significance test performed by the organizers. Nevertheless, we
obtain even better post-competition results (a macro-F1 score of 62.28%) using
the audio embeddings released by the organizers after the competition. With a
very similar approach (that did not include phonetic features), we also ranked
first in the ADI Closed Shared Tasks of the 2017 VarDial Evaluation Campaign,
surpassing the second best method by 4.62%. We therefore conclude that our
multiple kernel learning method is the best approach to date for Arabic dialect
identification.
| null |
http://arxiv.org/abs/1805.04876v4
|
http://arxiv.org/pdf/1805.04876v4.pdf
|
COLING 2018 8
|
[
"Andrei M. Butnaru",
"Radu Tudor Ionescu"
] |
[
"Dialect Identification"
] | 2018-05-13T00:00:00 |
https://aclanthology.org/W18-3909
|
https://aclanthology.org/W18-3909.pdf
|
unibuckernel-reloaded-first-place-in-arabic-1
| null |
[] |
https://paperswithcode.com/paper/an-analysis-of-scale-invariance-in-object-1
|
1711.08189
| null | null |
An Analysis of Scale Invariance in Object Detection - SNIP
|
An analysis of different techniques for recognizing and detecting objects
under extreme scale variation is presented. Scale specific and scale invariant
design of detectors are compared by training them with different configurations
of input data. By evaluating the performance of different network architectures
for classifying small objects on ImageNet, we show that CNNs are not robust to
changes in scale. Based on this analysis, we propose to train and test
detectors on the same scales of an image-pyramid. Since small and large objects
are difficult to recognize at smaller and larger scales respectively, we
present a novel training scheme called Scale Normalization for Image Pyramids
(SNIP) which selectively back-propagates the gradients of object instances of
different sizes as a function of the image scale. On the COCO dataset, our
single model performance is 45.7% and an ensemble of 3 networks obtains an mAP
of 48.3%. We use off-the-shelf ImageNet-1000 pre-trained models and only train
with bounding box supervision. Our submission won the Best Student Entry in the
COCO 2017 challenge. Code will be made available at
\url{http://bit.ly/2yXVg4c}.
| null |
http://arxiv.org/abs/1711.08189v2
|
http://arxiv.org/pdf/1711.08189v2.pdf
| null |
[
"Bharat Singh",
"Larry S. Davis"
] |
[
"Object",
"object-detection",
"Object Detection"
] | 2017-11-22T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**Average Pooling** is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs. It extracts features more smoothly than [Max Pooling](https://paperswithcode.com/method/max-pooling), whereas max pooling extracts more pronounced features like edges.\r\n\r\nImage Source: [here](https://www.researchgate.net/figure/Illustration-of-Max-Pooling-and-Average-Pooling-Figure-2-above-shows-an-example-of-max_fig2_333593451)",
"full_name": "Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Average Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/densenet.py#L113",
"description": "A **Concatenated Skip Connection** is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from previous layers of the network. This contrasts with say, residual connections, where element-wise summation is used instead to incorporate information from previous layers. This type of skip connection is prominently used in DenseNets (and also Inception networks), which the Figure to the right illustrates.",
"full_name": "Concatenated Skip Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Concatenated Skip Connection",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/prlz77/ResNeXt.pytorch/blob/39fb8d03847f26ec02fb9b880ecaaa88db7a7d16/models/model.py#L42",
"description": "A **Grouped Convolution** uses a group of convolutions - multiple kernels per layer - resulting in multiple channel outputs per layer. This leads to wider networks helping a network learn a varied set of low level and high level features. The original motivation of using Grouped Convolutions in [AlexNet](https://paperswithcode.com/method/alexnet) was to distribute the model over multiple GPUs as an engineering compromise. But later, with models such as [ResNeXt](https://paperswithcode.com/method/resnext), it was shown this module could be used to improve classification accuracy. Specifically by exposing a new dimension through grouped convolutions, *cardinality* (the size of set of transformations), we can increase accuracy by increasing it.",
"full_name": "Grouped Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Grouped Convolution",
"source_title": "ImageNet Classification with Deep Convolutional Neural Networks",
"source_url": "http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks"
},
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": null,
"description": "**Dense Connections**, or **Fully Connected Connections**, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output by a weight. This means there are $n\\_{\\text{inputs}}*n\\_{\\text{outputs}}$ parameters, which can lead to a lot of parameters for a sizeable network.\r\n\r\n$$h\\_{l} = g\\left(\\textbf{W}^{T}h\\_{l-1}\\right)$$\r\n\r\nwhere $g$ is an activation function.\r\n\r\nImage Source: Deep Learning by Goodfellow, Bengio and Courville",
"full_name": "Dense Connections",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which rely primarily on dense-like connections. Below you can find a continuously updating list of feedforward network components.",
"name": "Feedforward Networks",
"parent": null
},
"name": "Dense Connections",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/rwightman/pytorch-dpn-pretrained/blob/2923586d8f4ab3fdc05370cc409a620a3dbd1083/dpn.py#L205",
"description": "A **Dual Path Network** block is an image model block used in convolutional neural network. The idea of this module is to enable sharing of common features while maintaining the flexibility to explore new features through dual path architectures. In this sense it combines the benefits of [ResNets](https://paperswithcode.com/method/resnet) and [DenseNets](https://paperswithcode.com/method/densenet). It was proposed as part of the [DPN](https://paperswithcode.com/method/dpn) CNN architecture.\r\n\r\nWe formulate such a dual path architecture as follows:\r\n\r\n$$x^{k} = \\sum\\limits\\_{t=1}^{k-1} f\\_t^{k}(h^t) \\text{,} $$\r\n\r\n$$\r\ny^{k} = \\sum\\limits\\_{t=1}^{k-1} v\\_t(h^t) = y^{k-1} + \\phi^{k-1}(y^{k-1}) \\text{,} \\\\\\\\\r\n$$\r\n\r\n$$\r\nr^{k} = x^{k} + y^{k} \\text{,} \\\\\\\\\r\n$$\r\n\r\n$$\r\nh^k = g^k \\left( r^{k} \\right) \\text{,}\r\n$$\r\n\r\nwhere $x^{k}$ and $y^{k}$ denote the extracted information at $k$-th step from individual path, $v_t(\\cdot)$ is a feature learning function as $f_t^k(\\cdot)$. The first equation refers to the densely connected path that enables exploring new features. The second equation refers to the residual path that enables common features re-usage. The third equation defines the dual path that integrates them and feeds them to the last transformation function in the last equation.",
"full_name": "DPN Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "DPN Block",
"source_title": "Dual Path Networks",
"source_url": "http://arxiv.org/abs/1707.01629v2"
},
{
"code_snippet_url": "https://github.com/osmr/imgclsmob/blob/c03fa67de3c9e454e9b6d35fe9cbb6b15c28fda7/pytorch/pytorchcv/models/dpn.py#L322",
"description": "A **Dual Path Network (DPN)** is a convolutional neural network which presents a new topology of connection paths internally. The intuition is that [ResNets](https://paperswithcode.com/method/resnet) enables feature re-usage while [DenseNet](https://paperswithcode.com/method/densenet) enables new feature exploration, and both are important for learning good representations. To enjoy the benefits from both path topologies, Dual Path Networks share common features while maintaining the flexibility to explore new features through dual path architectures. \r\n\r\nWe formulate such a dual path architecture as follows:\r\n\r\n$$x^{k} = \\sum\\limits\\_{t=1}^{k-1} f\\_t^{k}(h^t) \\text{,} $$\r\n\r\n$$\r\ny^{k} = \\sum\\limits\\_{t=1}^{k-1} v\\_t(h^t) = y^{k-1} + \\phi^{k-1}(y^{k-1}) \\text{,} \\\\\\\\\r\n$$\r\n\r\n$$\r\nr^{k} = x^{k} + y^{k} \\text{,} \\\\\\\\\r\n$$\r\n\r\n$$\r\nh^k = g^k \\left( r^{k} \\right) \\text{,}\r\n$$\r\n\r\nwhere $x^{k}$ and $y^{k}$ denote the extracted information at $k$-th step from individual path, $v_t(\\cdot)$ is a feature learning function as $f_t^k(\\cdot)$. The first equation refers to the densely connected path that enables exploring new features. The second equation refers to the residual path that enables common features re-usage. The third equation defines the dual path that integrates them and feeds them to the last transformation function in the last equation.",
"full_name": "Dual Path Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "DPN",
"source_title": "Dual Path Networks",
"source_url": "http://arxiv.org/abs/1707.01629v2"
},
{
"code_snippet_url": null,
"description": "A **Region Proposal Network**, or **RPN**, is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals. RPN and algorithms like [Fast R-CNN](https://paperswithcode.com/method/fast-r-cnn) can be merged into a single network by sharing their convolutional features - using the recently popular terminology of neural networks with attention mechanisms, the RPN component tells the unified network where to look.\r\n\r\nRPNs are designed to efficiently predict region proposals with a wide range of scales and aspect ratios. RPNs use anchor boxes that serve as references at multiple scales and aspect ratios. The scheme can be thought of as a pyramid of regression references, which avoids enumerating images or filters of multiple scales or aspect ratios.",
"full_name": "Region Proposal Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Region Proposal",
"parent": null
},
"name": "RPN",
"source_title": "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks",
"source_url": "http://arxiv.org/abs/1506.01497v3"
},
{
"code_snippet_url": null,
"description": "Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box $M$ with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold)\r\nwith $M$ are suppressed. This process is recursively applied on the remaining boxes. As per the design of the algorithm, if an object lies within the predefined overlap threshold, it leads to a miss. \r\n\r\n**Soft-NMS** solves this problem by decaying the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process.",
"full_name": "Soft-NMS",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Proposal Filtering",
"parent": null
},
"name": "Soft-NMS",
"source_title": "Soft-NMS -- Improving Object Detection With One Line of Code",
"source_url": "http://arxiv.org/abs/1704.04503v2"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/971c3e45b96bc5aa5868c45cd40e4f3c3d90d126/torchvision/ops/ps_roi_pool.py#L10",
"description": "**Position-Sensitive RoI Pooling layer** aggregates the outputs of the last convolutional layer and generates scores for each RoI. Unlike [RoI Pooling](https://paperswithcode.com/method/roi-pooling), PS RoI Pooling conducts selective pooling, and each of the $k$ × $k$ bin aggregates responses from only one score map out of the bank of $k$ × $k$ score maps. With end-to-end training, this RoI layer shepherds the last convolutional layer to learn specialized position-sensitive score maps.",
"full_name": "Position-Sensitive RoI Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**RoI Feature Extractors** are used to extract regions of interest features for tasks such as object detection. Below you can find a continuously updating list of RoI Feature Extractors.",
"name": "RoI Feature Extractors",
"parent": null
},
"name": "Position-Sensitive RoI Pooling",
"source_title": "R-FCN: Object Detection via Region-based Fully Convolutional Networks",
"source_url": "http://arxiv.org/abs/1605.06409v2"
},
{
"code_snippet_url": "https://github.com/facebookresearch/Detectron/blob/8170b25b425967f8f1c7d715bea3c5b8d9536cd8/detectron/modeling/rfcn_heads.py",
"description": "**Region-based Fully Convolutional Networks**, or **R-FCNs**, are a type of region-based object detector. In contrast to previous region-based object detectors such as Fast/[Faster R-CNN](https://paperswithcode.com/method/faster-r-cnn) that apply a costly per-region subnetwork hundreds of times, R-FCN is fully convolutional with almost all computation shared on the entire image.\r\n\r\nTo achieve this, R-FCN utilises position-sensitive score maps to address a dilemma between translation-invariance in image classification and translation-variance in object detection.",
"full_name": "Region-based Fully Convolutional Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Object Detection Models** are architectures used to perform the task of object detection. Below you can find a continuously updating list of object detection models.",
"name": "Object Detection Models",
"parent": null
},
"name": "R-FCN",
"source_title": "R-FCN: Object Detection via Region-based Fully Convolutional Networks",
"source_url": "http://arxiv.org/abs/1605.06409v2"
},
{
"code_snippet_url": "https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/2f57c5db49161bd6c899670a5e4fba50e6b8fd26/modules/deform_conv.py#L10",
"description": "**Deformable convolutions** add 2D offsets to the regular grid sampling locations in the standard [convolution](https://paperswithcode.com/method/convolution). It enables free form deformation of the sampling grid. The offsets are learned from the preceding feature maps, via additional convolutional layers. Thus, the deformation is conditioned on the input features in a local, dense, and adaptive manner.",
"full_name": "Deformable Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Deformable Convolution",
"source_title": "Deformable Convolutional Networks",
"source_url": "http://arxiv.org/abs/1703.06211v3"
},
{
"code_snippet_url": null,
"description": "**SNIP**, or **Scale Normalization for Image Pyramids**, is a multi-scale training scheme that selectively back-propagates the gradients of object instances of different sizes as a function of the image scale. SNIP is a modified version of MST where only the object instances that have a resolution close to the pre-training dataset, which is typically 224x224, are used for training the detector. In multi-scale training (MST), each image is observed at different resolutions therefore, at a high resolution (like 1400x2000) large objects are hard to classify and at a low resolution (like 480x800) small objects are hard to classify. Fortunately, each object instance appears at several different scales and some of those appearances fall in the desired scale range. In order to eliminate extreme scale objects, either too large or too small, training is only performed on objects that fall in the desired scale range and the remainder are simply ignored during back-propagation. Effectively, SNIP uses all the object instances during training, which helps capture all the variations in appearance and\r\npose, while reducing the domain-shift in the scale-space for the pre-trained network.",
"full_name": "SNIP",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "",
"name": "Multi-Scale Training",
"parent": null
},
"name": "SNIP",
"source_title": "An Analysis of Scale Invariance in Object Detection - SNIP",
"source_url": "http://arxiv.org/abs/1711.08189v2"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/resnet.py#L118",
"description": "**Residual Connections** are a type of skip-connection that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. \r\n\r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers.",
"full_name": "Residual Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Residual Connection",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!\r\n\r\n\r\nHow Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** now to speak directly with a live Expedia agent and unlock exclusive best deal discounts on hotels, flights, and vacation packages. Get real help fast while enjoying limited-time offers that make your next trip more affordable, smooth, and stress-free. Don’t wait—call today!",
"full_name": "*Communicated@Fast*How Do I Communicate to Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "How do I escalate a problem with Expedia?\r\nTo escalate a problem with Expedia, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask to speak with a manager. Explain your issue in detail and inquire about compensation. Expedia may provide exclusive discount codes, travel credits, or special offers to help resolve your problem and improve your experience.\r\nIs Expedia actually fully refundable?\r\nExpedia isn’t always fully refundable—refunds depend on the hotel, airline, or rental provider’s policy call +1(888) (829) (0881) OR +1(805) (330) (4056). Look for “Free Cancellation” before booking to ensure flexibility. For peace of mind and potential savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about current discount codes or refund-friendly deals.\r\n\r\nWhat is the refundable option on expedia?\r\nThe refundable option on Expedia allows you to cancel eligible bookings call +1(888) (829) (0881) OR +1(805) (330) (4056) without penalty. Look for listings marked “Free Cancellation” or “Fully Refundable.” To maximize flexibility, choose these options during checkout. For additional savings, call +1(888) (829) (0881) OR +1(805) (330) (4056) and ask about exclusive promo codes or travel discounts available today.",
"name": "Activation Functions",
"parent": null
},
"name": "ReLU",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-linearity after convolutions. It maps an input pixel with all its channels to an output pixel which can be squeezed to a desired output depth. It can be viewed as an [MLP](https://paperswithcode.com/method/feedforward-network) looking at a particular pixel location.\r\n\r\nImage Credit: [http://deeplearning.ai](http://deeplearning.ai)",
"full_name": "1x1 Convolution",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "1x1 Convolution",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/google/jax/blob/36f91261099b00194922bd93ed1286fe1c199724/jax/experimental/stax.py#L116",
"description": "**Batch Normalization** aims to reduce internal covariate shift, and in doing so aims to accelerate the training of deep neural nets. It accomplishes this via a normalization step that fixes the means and variances of layer inputs. Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows for use of much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for [Dropout](https://paperswithcode.com/method/dropout).\r\n\r\nWe apply a batch normalization layer as follows for a minibatch $\\mathcal{B}$:\r\n\r\n$$ \\mu\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}x\\_{i} $$\r\n\r\n$$ \\sigma^{2}\\_{\\mathcal{B}} = \\frac{1}{m}\\sum^{m}\\_{i=1}\\left(x\\_{i}-\\mu\\_{\\mathcal{B}}\\right)^{2} $$\r\n\r\n$$ \\hat{x}\\_{i} = \\frac{x\\_{i} - \\mu\\_{\\mathcal{B}}}{\\sqrt{\\sigma^{2}\\_{\\mathcal{B}}+\\epsilon}} $$\r\n\r\n$$ y\\_{i} = \\gamma\\hat{x}\\_{i} + \\beta = \\text{BN}\\_{\\gamma, \\beta}\\left(x\\_{i}\\right) $$\r\n\r\nWhere $\\gamma$ and $\\beta$ are learnable parameters.",
"full_name": "Batch Normalization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Normalization** layers in deep learning are used to make optimization easier by smoothing the loss surface of the network. Below you will find a continuously updating list of normalization methods.",
"name": "Normalization",
"parent": null
},
"name": "Batch Normalization",
"source_title": "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift",
"source_url": "http://arxiv.org/abs/1502.03167v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L75",
"description": "A **Bottleneck Residual Block** is a variant of the [residual block](https://paperswithcode.com/method/residual-block) that utilises 1x1 convolutions to create a bottleneck. The use of a bottleneck reduces the number of parameters and matrix multiplications. The idea is to make residual blocks as thin as possible to increase depth and have less parameters. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture, and are used as part of deeper ResNets such as ResNet-50 and ResNet-101.",
"full_name": "Bottleneck Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Bottleneck Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/baa592b215804927e28638f6a7f3318cbc411d49/torchvision/models/resnet.py#L157",
"description": "**Global Average Pooling** is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding category of the classification task in the last mlpconv layer. Instead of adding fully connected layers on top of the feature maps, we take the average of each feature map, and the resulting vector is fed directly into the [softmax](https://paperswithcode.com/method/softmax) layer. \r\n\r\nOne advantage of global [average pooling](https://paperswithcode.com/method/average-pooling) over the fully connected layers is that it is more native to the [convolution](https://paperswithcode.com/method/convolution) structure by enforcing correspondences between feature maps and categories. Thus the feature maps can be easily interpreted as categories confidence maps. Another advantage is that there is no parameter to optimize in the global average pooling thus overfitting is avoided at this layer. Furthermore, global average pooling sums out the spatial information, thus it is more robust to spatial translations of the input.",
"full_name": "Global Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Global Average Pooling",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/resnet.py#L35",
"description": "**Residual Blocks** are skip-connection blocks that learn residual functions with reference to the layer inputs, instead of learning unreferenced functions. They were introduced as part of the [ResNet](https://paperswithcode.com/method/resnet) architecture.\r\n \r\nFormally, denoting the desired underlying mapping as $\\mathcal{H}({x})$, we let the stacked nonlinear layers fit another mapping of $\\mathcal{F}({x}):=\\mathcal{H}({x})-{x}$. The original mapping is recast into $\\mathcal{F}({x})+{x}$. The $\\mathcal{F}({x})$ acts like a residual, hence the name 'residual block'.\r\n\r\nThe intuition is that it is easier to optimize the residual mapping than to optimize the original, unreferenced mapping. To the extreme, if an identity mapping were optimal, it would be easier to push the residual to zero than to fit an identity mapping by a stack of nonlinear layers. Having skip connections allows the network to more easily learn identity-like mappings.\r\n\r\nNote that in practice, [Bottleneck Residual Blocks](https://paperswithcode.com/method/bottleneck-residual-block) are used for deeper ResNets, such as ResNet-50 and ResNet-101, as these bottleneck blocks are less computationally intensive.",
"full_name": "Residual Block",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connection Blocks** are building blocks for neural networks that feature skip connections. These skip connections 'skip' some layers allowing gradients to better flow through the network. Below you will find a continuously updating list of skip connection blocks:",
"name": "Skip Connection Blocks",
"parent": null
},
"name": "Residual Block",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/0adb5843766092fba584791af76383125fd0d01c/torch/nn/init.py#L389",
"description": "**Kaiming Initialization**, or **He Initialization**, is an initialization method for neural networks that takes into account the non-linearity of activation functions, such as [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nA proper initialization method should avoid reducing or magnifying the magnitudes of input signals exponentially. Using a derivation they work out that the condition to stop this happening is:\r\n\r\n$$\\frac{1}{2}n\\_{l}\\text{Var}\\left[w\\_{l}\\right] = 1 $$\r\n\r\nThis implies an initialization scheme of:\r\n\r\n$$ w\\_{l} \\sim \\mathcal{N}\\left(0, 2/n\\_{l}\\right)$$\r\n\r\nThat is, a zero-centered Gaussian with standard deviation of $\\sqrt{2/{n}\\_{l}}$ (variance shown in equation above). Biases are initialized at $0$.",
"full_name": "Kaiming Initialization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Initialization** methods are used to initialize the weights in a neural network. Below can you find a continuously updating list of initialization methods.",
"name": "Initialization",
"parent": null
},
"name": "Kaiming Initialization",
"source_title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
"source_url": "http://arxiv.org/abs/1502.01852v1"
},
{
"code_snippet_url": null,
"description": "**Max Pooling** is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs.\r\n\r\nImage Source: [here](https://computersciencewiki.org/index.php/File:MaxpoolSample2.png)",
"full_name": "Max Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Max Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Bitcoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Bitcoin transaction not confirmed, your Bitcoin wallet not showing balance, or you're trying to recover a lost Bitcoin wallet, knowing where to get help is essential. That’s why the Bitcoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Bitcoin Customer Support Number +1-833-534-1729\r\nBitcoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Bitcoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Bitcoin transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Bitcoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Bitcoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Bitcoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Bitcoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Bitcoin Deposit Not Received\r\nIf someone has sent you Bitcoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Bitcoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Bitcoin Transaction Stuck or Pending\r\nSometimes your Bitcoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Bitcoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Bitcoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Bitcoin Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Bitcoin tech.\r\n\r\n24/7 Availability: Bitcoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Bitcoin Support and Wallet Issues\r\nQ1: Can Bitcoin support help me recover stolen BTC?\r\nA: While Bitcoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Bitcoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Bitcoin’s official number (Bitcoin is decentralized), it connects you to trained professionals experienced in resolving all major Bitcoin issues.\r\n\r\nFinal Thoughts\r\nBitcoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Bitcoin transaction not confirmed, your Bitcoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Bitcoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Bitcoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "Bitcoin Customer Service Number +1-833-534-1729",
"source_title": "Deep Residual Learning for Image Recognition",
"source_url": "http://arxiv.org/abs/1512.03385v1"
}
] |
https://paperswithcode.com/paper/double-quantization-for-communication
|
1805.10111
| null | null |
Double Quantization for Communication-Efficient Distributed Optimization
|
Modern distributed training of machine learning models suffers from high communication overhead for synchronizing stochastic gradients and model parameters. In this paper, to reduce the communication complexity, we propose \emph{double quantization}, a general scheme for quantizing both model parameters and gradients. Three communication-efficient algorithms are proposed under this general scheme. Specifically, (i) we propose a low-precision algorithm AsyLPG with asynchronous parallelism, (ii) we explore integrating gradient sparsification with double quantization and develop Sparse-AsyLPG, (iii) we show that double quantization can also be accelerated by momentum technique and design accelerated AsyLPG. We establish rigorous performance guarantees for the algorithms, and conduct experiments on a multi-server test-bed to demonstrate that our algorithms can effectively save transmitted bits without performance degradation.
| null |
https://arxiv.org/abs/1805.10111v4
|
https://arxiv.org/pdf/1805.10111v4.pdf
|
NeurIPS 2019 12
|
[
"Yue Yu",
"Jiaxiang Wu",
"Longbo Huang"
] |
[
"Distributed Optimization",
"Quantization"
] | 2018-05-25T00:00:00 |
http://papers.nips.cc/paper/8694-double-quantization-for-communication-efficient-distributed-optimization
|
http://papers.nips.cc/paper/8694-double-quantization-for-communication-efficient-distributed-optimization.pdf
|
double-quantization-for-communication-1
| null |
[
{
"code_snippet_url": "",
"description": "**Gradient Sparsification** is a technique for distributed training that sparsifies stochastic gradients to reduce the communication cost, with minor increase in the number of iterations. The key idea behind our sparsification technique is to drop some coordinates of the stochastic gradient and appropriately amplify the remaining coordinates to ensure the unbiasedness of the sparsified stochastic gradient. The sparsification approach can significantly reduce the coding length of the stochastic gradient and only slightly increase the variance of the stochastic gradient.",
"full_name": "Gradient Sparsification",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "This section contains a compilation of distributed methods for scaling deep learning to very large models. There are many different strategies for scaling training across multiple devices, including:\r\n\r\n - [Data Parallel](https://paperswithcode.com/methods/category/data-parallel-methods) : for each node we use the same model parameters to do forward propagation, but we send a small batch of different data to each node, compute the gradient normally, and send it back to the main node. Once we have all the gradients, we calculate the weighted average and use this to update the model parameters.\r\n\r\n - [Model Parallel](https://paperswithcode.com/methods/category/model-parallel-methods) : for each node we assign different layers to it. During forward propagation, we start in the node with the first layers, then move onto the next, and so on. Once forward propagation is done we calculate gradients for the last node, and update model parameters for that node. Then we backpropagate onto the penultimate node, update the parameters, and so on.\r\n\r\n - Additional methods including [Hybrid Parallel](https://paperswithcode.com/methods/category/hybrid-parallel-methods), [Auto Parallel](https://paperswithcode.com/methods/category/auto-parallel-methods), and [Distributed Communication](https://paperswithcode.com/methods/category/distributed-communication).\r\n\r\nImage credit: [Jordi Torres](https://towardsdatascience.com/scalable-deep-learning-on-parallel-and-distributed-infrastructures-e5fb4a956bef).",
"name": "Distributed Methods",
"parent": null
},
"name": "Gradient Sparsification",
"source_title": "Gradient Sparsification for Communication-Efficient Distributed Optimization",
"source_url": "http://arxiv.org/abs/1710.09854v1"
}
] |
https://paperswithcode.com/paper/the-logistic-network-lasso
|
1805.02483
| null | null |
The Logistic Network Lasso
|
We apply the network Lasso to solve binary classification and clustering
problems for network-structured data. To this end, we generalize ordinary
logistic regression to non-Euclidean data with an intrinsic network structure.
The resulting "logistic network Lasso" amounts to solving a non-smooth convex
regularized empirical risk minimization. The risk is measured using the
logistic loss incurred over a small set of labeled nodes. For the
regularization, we propose to use the total variation of the classifier
requiring it to conform to the underlying network structure. A scalable
implementation of the learning method is obtained using an inexact variant of
the alternating direction methods of multipliers which results in a scalable
learning algorithm
| null |
http://arxiv.org/abs/1805.02483v4
|
http://arxiv.org/pdf/1805.02483v4.pdf
| null |
[
"Henrik Ambos",
"Nguyen Tran",
"Alexander Jung"
] |
[
"Binary Classification",
"Clustering",
"General Classification",
"regression"
] | 2018-05-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/uplift-modeling-from-separate-labels
|
1803.05112
| null | null |
Uplift Modeling from Separate Labels
|
Uplift modeling is aimed at estimating the incremental impact of an action on
an individual's behavior, which is useful in various application domains such
as targeted marketing (advertisement campaigns) and personalized medicine
(medical treatments). Conventional methods of uplift modeling require every
instance to be jointly equipped with two types of labels: the taken action and
its outcome. However, obtaining two labels for each instance at the same time
is difficult or expensive in many real-world problems. In this paper, we
propose a novel method of uplift modeling that is applicable to a more
practical setting where only one type of labels is available for each instance.
We show a mean squared error bound for the proposed estimator and demonstrate
its effectiveness through experiments.
|
Uplift modeling is aimed at estimating the incremental impact of an action on an individual's behavior, which is useful in various application domains such as targeted marketing (advertisement campaigns) and personalized medicine (medical treatments).
|
http://arxiv.org/abs/1803.05112v5
|
http://arxiv.org/pdf/1803.05112v5.pdf
|
NeurIPS 2018 12
|
[
"Ikko Yamane",
"Florian Yger",
"Jamal Atif",
"Masashi Sugiyama"
] |
[
"Marketing"
] | 2018-03-14T00:00:00 |
http://papers.nips.cc/paper/8198-uplift-modeling-from-separate-labels
|
http://papers.nips.cc/paper/8198-uplift-modeling-from-separate-labels.pdf
|
uplift-modeling-from-separate-labels-1
| null |
[] |
https://paperswithcode.com/paper/generating-protected-fingerprint-template
|
1805.10108
| null | null |
Generating protected fingerprint template utilizing coprime mapping transformation
|
The identity of a user is permanently lost if biometric data gets compromised
since the biometric information is irreplaceable and irrevocable. To revoke and
reissue a new template in place of the compromised biometric template, the idea
of cancelable biometrics has been introduced. The concept behind cancelable
biometric is to irreversibly transform the original biometric template and
perform the comparison in the protected domain. In this paper, a coprime
transformation scheme has been proposed to derive a protected fingerprint
template. The method divides the fingerprint region into a number of sectors
with respect to each minutiae point and identifies the nearest-neighbor
minutiae in each sector. Then, ridge features for all neighboring minutiae
points are computed and mapped onto co-prime positions of a random matrix to
generate the cancelable template. The proposed approach achieves an EER of
1.82, 1.39, 4.02 and 5.77 on DB1, DB2, DB3 and DB4 datasets of the FVC2002 and
an EER of 8.70, 7.95, 5.23 and 4.87 on DB1, DB2, DB3 and DB4 datasets of
FVC2004 databases, respectively. Experimental evaluations indicate that the
method outperforms in comparison to the current state-of-the-art. Moreover, it
has been confirmed from the security analysis that the proposed method fulfills
the desired characteristics of diversity, revocability, and non-invertibility
with a minor performance degradation caused by the transformation.
| null |
http://arxiv.org/abs/1805.10108v1
|
http://arxiv.org/pdf/1805.10108v1.pdf
| null |
[
"Rudresh Dwivedi",
"Somnath Dey"
] |
[] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/underwater-fish-species-classification-using
|
1805.10106
| null | null |
Underwater Fish Species Classification using Convolutional Neural Network and Deep Learning
|
The target of this paper is to recommend a way for Automated classification
of Fish species. A high accuracy fish classification is required for greater
understanding of fish behavior in Ichthyology and by marine biologists.
Maintaining a ledger of the number of fishes per species and marking the
endangered species in large and small water bodies is required by concerned
institutions. Majority of available methods focus on classification of fishes
outside of water because underwater classification poses challenges such as
background noises, distortion of images, the presence of other water bodies in
images, image quality and occlusion. This method uses a novel technique based
on Convolutional Neural Networks, Deep Learning and Image Processing to achieve
an accuracy of 96.29%. This method ensures considerably discrimination accuracy
improvements than the previously proposed methods.
| null |
http://arxiv.org/abs/1805.10106v1
|
http://arxiv.org/pdf/1805.10106v1.pdf
| null |
[
"Dhruv Rathi",
"Sushant Jain",
"Dr. S. Indu"
] |
[
"Classification",
"General Classification"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-1-evolutionary-algorithm-with-self
|
1704.02191
| null | null |
The (1+$λ$) Evolutionary Algorithm with Self-Adjusting Mutation Rate
|
We propose a new way to self-adjust the mutation rate in population-based
evolutionary algorithms in discrete search spaces. Roughly speaking, it
consists of creating half the offspring with a mutation rate that is twice the
current mutation rate and the other half with half the current rate. The
mutation rate is then updated to the rate used in that subpopulation which
contains the best offspring.
We analyze how the $(1+\lambda)$ evolutionary algorithm with this
self-adjusting mutation rate optimizes the OneMax test function. We prove that
this dynamic version of the $(1+\lambda)$ EA finds the optimum in an expected
optimization time (number of fitness evaluations) of
$O(n\lambda/\log\lambda+n\log n)$. This time is asymptotically smaller than the
optimization time of the classic $(1+\lambda)$ EA. Previous work shows that
this performance is best-possible among all $\lambda$-parallel mutation-based
unbiased black-box algorithms.
This result shows that the new way of adjusting the mutation rate can find
optimal dynamic parameter values on the fly. Since our adjustment mechanism is
simpler than the ones previously used for adjusting the mutation rate and does
not have parameters itself, we are optimistic that it will find other
applications.
| null |
http://arxiv.org/abs/1704.02191v3
|
http://arxiv.org/pdf/1704.02191v3.pdf
| null |
[
"Benjamin Doerr",
"Christian Gießen",
"Carsten Witt",
"Jing Yang"
] |
[
"Evolutionary Algorithms"
] | 2017-04-07T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/recursive-nonlinear-system-identification
|
1606.04366
| null | null |
Recursive nonlinear-system identification using latent variables
|
In this paper we develop a method for learning nonlinear systems with
multiple outputs and inputs. We begin by modelling the errors of a nominal
predictor of the system using a latent variable framework. Then using the
maximum likelihood principle we derive a criterion for learning the model. The
resulting optimization problem is tackled using a majorization-minimization
approach. Finally, we develop a convex majorization technique and show that it
enables a recursive identification method. The method learns parsimonious
predictive models and is tested on both synthetic and real nonlinear systems.
|
In this paper we develop a method for learning nonlinear systems with multiple outputs and inputs.
|
http://arxiv.org/abs/1606.04366v3
|
http://arxiv.org/pdf/1606.04366v3.pdf
| null |
[
"Per Mattsson",
"Dave Zachariah",
"Petre Stoica"
] |
[] | 2016-06-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-double-deep-spatio-angular-learning
|
1805.10078
| null | null |
A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition
|
Face recognition has attracted increasing attention due to its wide range of
applications, but it is still challenging when facing large variations in the
biometric data characteristics. Lenslet light field cameras have recently come
into prominence to capture rich spatio-angular information, thus offering new
possibilities for advanced biometric recognition systems. This paper proposes a
double-deep spatio-angular learning framework for light field based face
recognition, which is able to learn both texture and angular dynamics in
sequence using convolutional representations; this is a novel recognition
framework that has never been proposed before for either face recognition or
any other visual recognition task. The proposed double-deep learning framework
includes a long short-term memory (LSTM) recurrent network whose inputs are
VGG-Face descriptions that are computed using a VGG-Very-Deep-16 convolutional
neural network (CNN). The VGG-16 network uses different face viewpoints
rendered from a full light field image, which are organised as a pseudo-video
sequence. A comprehensive set of experiments has been conducted with the
IST-EURECOM light field face database, for varied and challenging recognition
tasks. Results show that the proposed framework achieves superior face
recognition performance when compared to the state-of-the-art.
| null |
http://arxiv.org/abs/1805.10078v3
|
http://arxiv.org/pdf/1805.10078v3.pdf
| null |
[
"Alireza Sepas-Moghaddam",
"Mohammad A. Haque",
"Paulo Lobato Correia",
"Kamal Nasrollahi",
"Thomas B. Moeslund",
"Fernando Pereira"
] |
[
"Face Recognition"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/statistical-optimality-of-stochastic-gradient
|
1805.10074
| null | null |
Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
|
We consider stochastic gradient descent (SGD) for least-squares regression
with potentially several passes over the data. While several passes have been
widely reported to perform practically better in terms of predictive
performance on unseen data, the existing theoretical analysis of SGD suggests
that a single pass is statistically optimal. While this is true for
low-dimensional easy problems, we show that for hard problems, multiple passes
lead to statistically optimal predictions while single pass does not; we also
show that in these hard models, the optimal number of passes over the data
increases with sample size. In order to define the notion of hardness and show
that our predictive performances are optimal, we consider potentially
infinite-dimensional models and notions typically associated to kernel methods,
namely, the decay of eigenvalues of the covariance matrix of the features and
the complexity of the optimal predictor as measured through the covariance
matrix. We illustrate our results on synthetic experiments with non-linear
kernel methods and on a classical benchmark with a linear model.
| null |
http://arxiv.org/abs/1805.10074v3
|
http://arxiv.org/pdf/1805.10074v3.pdf
|
NeurIPS 2018 12
|
[
"Loucas Pillaud-Vivien",
"Alessandro Rudi",
"Francis Bach"
] |
[] | 2018-05-25T00:00:00 |
http://papers.nips.cc/paper/8034-statistical-optimality-of-stochastic-gradient-descent-on-hard-learning-problems-through-multiple-passes
|
http://papers.nips.cc/paper/8034-statistical-optimality-of-stochastic-gradient-descent-on-hard-learning-problems-through-multiple-passes.pdf
|
statistical-optimality-of-stochastic-gradient-1
| null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using all available data. That is for weights $w$ and a loss function $L$ we have:\r\n\r\n$$ w\\_{t+1} = w\\_{t} - \\eta\\hat{\\nabla}\\_{w}{L(w\\_{t})} $$\r\n\r\nWhere $\\eta$ is a learning rate. SGD reduces redundancy compared to batch gradient descent - which recomputes gradients for similar examples before each parameter update - so it is usually much faster.\r\n\r\n(Image Source: [here](http://rasbt.github.io/mlxtend/user_guide/general_concepts/gradient-optimization/))",
"full_name": "Stochastic Gradient Descent",
"introduced_year": 1951,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "SGD",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/a-sliding-window-algorithm-for-markov
|
1805.10066
| null | null |
A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions
|
We consider reinforcement learning in changing Markov Decision Processes
where both the state-transition probabilities and the reward functions may vary
over time. For this problem setting, we propose an algorithm using a sliding
window approach and provide performance guarantees for the regret evaluated
against the optimal non-stationary policy. We also characterize the optimal
window size suitable for our algorithm. These results are complemented by a
sample complexity bound on the number of sub-optimal steps taken by the
algorithm. Finally, we present some experimental results to support our
theoretical analysis.
| null |
http://arxiv.org/abs/1805.10066v1
|
http://arxiv.org/pdf/1805.10066v1.pdf
| null |
[
"Pratik Gajane",
"Ronald Ortner",
"Peter Auer"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/incorporating-literals-into-knowledge-graph
|
1802.00934
| null | null |
Incorporating Literals into Knowledge Graph Embeddings
|
Knowledge graphs, on top of entities and their relationships, contain other important elements: literals. Literals encode interesting properties (e.g. the height) of entities that are not captured by links between entities alone. Most of the existing work on embedding (or latent feature) based knowledge graph analysis focuses mainly on the relations between entities. In this work, we study the effect of incorporating literal information into existing link prediction methods. Our approach, which we name LiteralE, is an extension that can be plugged into existing latent feature methods. LiteralE merges entity embeddings with their literal information using a learnable, parametrized function, such as a simple linear or nonlinear transformation, or a multilayer neural network. We extend several popular embedding models based on LiteralE and evaluate their performance on the task of link prediction. Despite its simplicity, LiteralE proves to be an effective way to incorporate literal information into existing embedding based methods, improving their performance on different standard datasets, which we augmented with their literals and provide as testbed for further research.
|
Most of the existing work on embedding (or latent feature) based knowledge graph analysis focuses mainly on the relations between entities.
|
https://arxiv.org/abs/1802.00934v3
|
https://arxiv.org/pdf/1802.00934v3.pdf
| null |
[
"Agustinus Kristiadi",
"Mohammad Asif Khan",
"Denis Lukovnikov",
"Jens Lehmann",
"Asja Fischer"
] |
[
"Entity Embeddings",
"Knowledge Graph Embeddings",
"Knowledge Graphs",
"Link Prediction"
] | 2018-02-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/unsupervisedly-training-gans-for-segmenting
|
1805.10059
| null | null |
Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations
|
Recently, generative adversarial networks exhibited excellent performances in
semi-supervised image analysis scenarios. In this paper, we go even further by
proposing a fully unsupervised approach for segmentation applications with
prior knowledge of the objects' shapes. We propose and investigate different
strategies to generate simulated label data and perform image-to-image
translation between the image and the label domain using an adversarial model.
Specifically, we assess the impact of the annotation model's accuracy as well
as the effect of simulating additional low-level image features. For
experimental evaluation, we consider the segmentation of the glomeruli, an
application scenario from renal pathology. Experiments provide proof of concept
and also confirm that the strategy for creating the simulated label data is of
particular relevance considering the stability of GAN trainings.
| null |
http://arxiv.org/abs/1805.10059v2
|
http://arxiv.org/pdf/1805.10059v2.pdf
| null |
[
"Michael Gadermayr",
"Laxmi Gupta",
"Barbara M. Klinkhammer",
"Peter Boor",
"Dorit Merhof"
] |
[
"Image-to-Image Translation",
"Segmentation",
"Translation"
] | 2018-05-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, Dogecoin has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're trying to recover a lost Dogecoin wallet, knowing where to get help is essential. That’s why the Dogecoin customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Dogecoin Customer Support Number +1-833-534-1729\r\nDogecoin operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Dogecoin Transaction Not Confirmed\r\nOne of the most common concerns is when a Dogecoin transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Dogecoin Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Dogecoin wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Dogecoin Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Dogecoin wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Dogecoin Deposit Not Received\r\nIf someone has sent you Dogecoin but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Dogecoin deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Dogecoin Transaction Stuck or Pending\r\nSometimes your Dogecoin transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Dogecoin Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Dogecoin wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Dogecoin Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Dogecoin tech.\r\n\r\n24/7 Availability: Dogecoin doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Dogecoin Support and Wallet Issues\r\nQ1: Can Dogecoin support help me recover stolen BTC?\r\nA: While Dogecoin transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Dogecoin transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Dogecoin’s official number (Dogecoin is decentralized), it connects you to trained professionals experienced in resolving all major Dogecoin issues.\r\n\r\nFinal Thoughts\r\nDogecoin is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Dogecoin transaction not confirmed, your Dogecoin wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Dogecoin customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Dogecoin Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Dogecoin Customer Service Number +1-833-534-1729",
"source_title": "Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1406.2661v1"
}
] |
https://paperswithcode.com/paper/em-algorithms-for-ica
|
1805.10054
| null | null |
Stochastic algorithms with descent guarantees for ICA
|
Independent component analysis (ICA) is a widespread data exploration technique, where observed signals are modeled as linear mixtures of independent components. From a machine learning point of view, it amounts to a matrix factorization problem with a statistical independence criterion. Infomax is one of the most used ICA algorithms. It is based on a loss function which is a non-convex log-likelihood. We develop a new majorization-minimization framework adapted to this loss function. We derive an online algorithm for the streaming setting, and an incremental algorithm for the finite sum setting, with the following benefits. First, unlike most algorithms found in the literature, the proposed methods do not rely on any critical hyper-parameter like a step size, nor do they require a line-search technique. Second, the algorithm for the finite sum setting, although stochastic, guarantees a decrease of the loss function at each iteration. Experiments demonstrate progress on the state-of-the-art for large scale datasets, without the necessity for any manual parameter tuning.
|
We derive an online algorithm for the streaming setting, and an incremental algorithm for the finite sum setting, with the following benefits.
|
https://arxiv.org/abs/1805.10054v2
|
https://arxiv.org/pdf/1805.10054v2.pdf
| null |
[
"Pierre Ablin",
"Alexandre Gramfort",
"Jean-François Cardoso",
"Francis Bach"
] |
[] | 2018-05-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "_**Independent component analysis** (ICA) is a statistical and computational technique for revealing hidden factors that underlie sets of random variables, measurements, or signals._\r\n\r\n_ICA defines a generative model for the observed multivariate data, which is typically given as a large database of samples. In the model, the data variables are assumed to be linear mixtures of some unknown latent variables, and the mixing system is also unknown. The latent variables are assumed nongaussian and mutually independent, and they are called the independent components of the observed data. These independent components, also called sources or factors, can be found by ICA._\r\n\r\n_ICA is superficially related to principal component analysis and factor analysis. ICA is a much more powerful technique, however, capable of finding the underlying factors or sources when these classic methods fail completely._\r\n\r\n\r\nExtracted from (https://www.cs.helsinki.fi/u/ahyvarin/whatisica.shtml)\r\n\r\n**Source papers**:\r\n\r\n[Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture](https://doi.org/10.1016/0165-1684(91)90079-X)\r\n\r\n[Independent component analysis, A new concept?](https://doi.org/10.1016/0165-1684(94)90029-9)\r\n\r\n[Independent component analysis: algorithms and applications](https://doi.org/10.1016/S0893-6080(00)00026-5)",
"full_name": "Independent Component Analysis",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Dimensionality Reduction** methods transform data from a high-dimensional space into a low-dimensional space so that the low-dimensional space retains the most important properties of the original data. Below you can find a continuously updating list of dimensionality reduction methods.",
"name": "Dimensionality Reduction",
"parent": null
},
"name": "ICA",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/bayesian-estimation-for-large-scale
|
1805.10050
| null | null |
Bayesian estimation for large scale multivariate Ornstein-Uhlenbeck model of brain connectivity
|
Estimation of reliable whole-brain connectivity is a crucial step towards the
use of connectivity information in quantitative approaches to the study of
neuropsychiatric disorders. When estimating brain connectivity a challenge is
imposed by the paucity of time samples and the large dimensionality of the
measurements. Bayesian estimation methods for network models offer a number of
advantages in this context but are not commonly employed. Here we compare three
different estimation methods for the multivariate Ornstein-Uhlenbeck model,
that has recently gained some popularity for characterizing whole-brain
connectivity. We first show that a Bayesian estimation of model parameters
assuming uniform priors is equivalent to an application of the method of
moments. Then, using synthetic data, we show that the Bayesian estimate scales
poorly with number of nodes in the network as compared to an iterative Lyapunov
optimization. In particular when the network size is in the order of that used
for whole-brain studies (about 100 nodes) the Bayesian method needs about eight
times more time samples than Lyapunov method in order to achieve similar
estimation accuracy. We also show that the higher estimation accuracy of
Lyapunov method is reflected in a much better classification of individuals
based on the estimated connectivity from a real dataset of BOLD fMRI. Finally
we show that the poor accuracy of Bayesian method is due to numerical errors,
when the imaginary part of the connectivity estimate gets large compared to its
real part.
| null |
http://arxiv.org/abs/1805.10050v1
|
http://arxiv.org/pdf/1805.10050v1.pdf
| null |
[
"Andrea Insabato",
"John P. Cunningham",
"Matthieu Gilson"
] |
[] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/japanese-predicate-conjugation-for-neural
|
1805.10047
| null | null |
Japanese Predicate Conjugation for Neural Machine Translation
|
Neural machine translation (NMT) has a drawback in that can generate only
high-frequency words owing to the computational costs of the softmax function
in the output layer.
In Japanese-English NMT, Japanese predicate conjugation causes an increase in
vocabulary size. For example, one verb can have as many as 19 surface
varieties. In this research, we focus on predicate conjugation for compressing
the vocabulary size in Japanese. The vocabulary list is filled with the various
forms of verbs. We propose methods using predicate conjugation information
without discarding linguistic information. The proposed methods can generate
low-frequency words and deal with unknown words. Two methods were considered to
introduce conjugation information: the first considers it as a token
(conjugation token) and the second considers it as an embedded vector
(conjugation feature).
The results using these methods demonstrate that the vocabulary size can be
compressed by approximately 86.1% (Tanaka corpus) and the NMT models can output
the words not in the training data set. Furthermore, BLEU scores improved by
0.91 points in Japanese-to-English translation, and 0.32 points in
English-to-Japanese translation with ASPEC.
| null |
http://arxiv.org/abs/1805.10047v1
|
http://arxiv.org/pdf/1805.10047v1.pdf
|
NAACL 2018 6
|
[
"Michiki Kurosawa",
"Yukio Matsumura",
"Hayahide Yamagishi",
"Mamoru Komachi"
] |
[
"Machine Translation",
"NMT",
"Translation"
] | 2018-05-25T00:00:00 |
https://aclanthology.org/N18-4014
|
https://aclanthology.org/N18-4014.pdf
|
japanese-predicate-conjugation-for-neural-1
| null |
[
{
"code_snippet_url": null,
"description": "The **Softmax** output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$ and a weighting vector $w$ we have:\r\n\r\n$$ P(y=j \\mid{x}) = \\frac{e^{x^{T}w_{j}}}{\\sum^{K}_{k=1}e^{x^{T}wk}} $$",
"full_name": "Softmax",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Output functions** are layers used towards the end of a network to transform to the desired form for a loss function. For example, the softmax relies on logits to construct a conditional probability. Below you can find a continuously updating list of output functions.",
"name": "Output Functions",
"parent": null
},
"name": "Softmax",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/struc2gauss-structure-preserving-network
|
1805.10043
| null | null |
struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding
|
Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art NE methods: role preservation and uncertainty modeling. Almost all previous methods represent a node into a point in space and focus on local structural information, i.e., neighborhood information. However, neighborhood information does not capture global structural information and point vector representation fails in modeling the uncertainty of node representations. In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information. struc2gauss first employs a given node similarity metric to measure the global structural information, then generates structural context for nodes and finally learns node representations via Gaussian embedding. Different structural similarity measures of networks and energy functions of Gaussian embedding are investigated. Experiments conducted on real-world networks demonstrate that struc2gauss effectively captures global structural information while state-of-the-art network embedding methods fail to, outperforms other methods on the structure-based clustering and classification task and provides more information on uncertainties of node representations.
| null |
https://arxiv.org/abs/1805.10043v2
|
https://arxiv.org/pdf/1805.10043v2.pdf
| null |
[
"Yulong Pei",
"Xin Du",
"Jianpeng Zhang",
"George Fletcher",
"Mykola Pechenizkiy"
] |
[
"Clustering",
"Network Embedding"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-dual-framework-for-low-rank-tensor
|
1712.01193
| null | null |
A dual framework for low-rank tensor completion
|
One of the popular approaches for low-rank tensor completion is to use the
latent trace norm regularization. However, most existing works in this
direction learn a sparse combination of tensors. In this work, we fill this gap
by proposing a variant of the latent trace norm that helps in learning a
non-sparse combination of tensors. We develop a dual framework for solving the
low-rank tensor completion problem. We first show a novel characterization of
the dual solution space with an interesting factorization of the optimal
solution. Overall, the optimal solution is shown to lie on a Cartesian product
of Riemannian manifolds. Furthermore, we exploit the versatile Riemannian
optimization framework for proposing computationally efficient trust region
algorithm. The experiments illustrate the efficacy of the proposed algorithm on
several real-world datasets across applications.
| null |
http://arxiv.org/abs/1712.01193v4
|
http://arxiv.org/pdf/1712.01193v4.pdf
|
NeurIPS 2018 12
|
[
"Madhav Nimishakavi",
"Pratik Jawanpuria",
"Bamdev Mishra"
] |
[
"Riemannian optimization"
] | 2017-12-04T00:00:00 |
http://papers.nips.cc/paper/7793-a-dual-framework-for-low-rank-tensor-completion
|
http://papers.nips.cc/paper/7793-a-dual-framework-for-low-rank-tensor-completion.pdf
|
a-dual-framework-for-low-rank-tensor-1
| null |
[] |
https://paperswithcode.com/paper/graph-bayesian-optimization-algorithms
|
1805.01157
| null | null |
Graph Bayesian Optimization: Algorithms, Evaluations and Applications
|
Network structure optimization is a fundamental task in complex network
analysis. However, almost all the research on Bayesian optimization is aimed at
optimizing the objective functions with vectorial inputs. In this work, we
first present a flexible framework, denoted graph Bayesian optimization, to
handle arbitrary graphs in the Bayesian optimization community. By combining
the proposed framework with graph kernels, it can take full advantage of
implicit graph structural features to supplement explicit features guessed
according to the experience, such as tags of nodes and any attributes of
graphs. The proposed framework can identify which features are more important
during the optimization process. We apply the framework to solve four problems
including two evaluations and two applications to demonstrate its efficacy and
potential applications.
| null |
http://arxiv.org/abs/1805.01157v4
|
http://arxiv.org/pdf/1805.01157v4.pdf
| null |
[
"Jiaxu Cui",
"Bo Yang"
] |
[
"Bayesian Optimization"
] | 2018-05-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/zeno-byzantine-suspicious-stochastic-gradient
|
1805.10032
| null | null |
Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance
|
We present Zeno, a technique to make distributed machine learning, particularly Stochastic Gradient Descent (SGD), tolerant to an arbitrary number of faulty workers. Zeno generalizes previous results that assumed a majority of non-faulty nodes; we need assume only one non-faulty worker. Our key idea is to suspect workers that are potentially defective. Since this is likely to lead to false positives, we use a ranking-based preference mechanism. We prove the convergence of SGD for non-convex problems under these scenarios. Experimental results show that Zeno outperforms existing approaches.
|
We present Zeno, a technique to make distributed machine learning, particularly Stochastic Gradient Descent (SGD), tolerant to an arbitrary number of faulty workers.
|
https://arxiv.org/abs/1805.10032v3
|
https://arxiv.org/pdf/1805.10032v3.pdf
| null |
[
"Cong Xie",
"Oluwasanmi Koyejo",
"Indranil Gupta"
] |
[
"BIG-bench Machine Learning"
] | 2018-05-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the full gradient using all available data. That is for weights $w$ and a loss function $L$ we have:\r\n\r\n$$ w\\_{t+1} = w\\_{t} - \\eta\\hat{\\nabla}\\_{w}{L(w\\_{t})} $$\r\n\r\nWhere $\\eta$ is a learning rate. SGD reduces redundancy compared to batch gradient descent - which recomputes gradients for similar examples before each parameter update - so it is usually much faster.\r\n\r\n(Image Source: [here](http://rasbt.github.io/mlxtend/user_guide/general_concepts/gradient-optimization/))",
"full_name": "Stochastic Gradient Descent",
"introduced_year": 1951,
"main_collection": {
"area": "General",
"description": "**Stochastic Optimization** methods are used to optimize neural networks. We typically take a mini-batch of data, hence 'stochastic', and perform a type of gradient descent with this minibatch. Below you can find a continuously updating list of stochastic optimization algorithms.",
"name": "Stochastic Optimization",
"parent": "Optimization"
},
"name": "SGD",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/dif-dataset-of-intoxicated-faces-for-drunk
|
1805.10030
| null | null |
DIF : Dataset of Perceived Intoxicated Faces for Drunk Person Identification
|
Traffic accidents cause over a million deaths every year, of which a large fraction is attributed to drunk driving. An automated intoxicated driver detection system in vehicles will be useful in reducing accidents and related financial costs. Existing solutions require special equipment such as electrocardiogram, infrared cameras or breathalyzers. In this work, we propose a new dataset called DIF (Dataset of perceived Intoxicated Faces) which contains audio-visual data of intoxicated and sober people obtained from online sources. To the best of our knowledge, this is the first work for automatic bimodal non-invasive intoxication detection. Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) are trained for computing the video and audio baselines, respectively. 3D CNN is used to exploit the Spatio-temporal changes in the video. A simple variation of the traditional 3D convolution block is proposed based on inducing non-linearity between the spatial and temporal channels. Extensive experiments are performed to validate the approach and baselines.
| null |
https://arxiv.org/abs/1805.10030v3
|
https://arxiv.org/pdf/1805.10030v3.pdf
| null |
[
"Vineet Mehta",
"Devendra Pratap Yadav",
"Sai Srinadhu Katta",
"Abhinav Dhall"
] |
[
"Person Identification"
] | 2018-05-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/73642d9425a358b51a683cf6f95852d06cba1096/torch/nn/modules/conv.py#L421",
"description": "A **3D Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) where the kernel slides in 3 dimensions as opposed to 2 dimensions with 2D convolutions. One example use case is medical imaging where a model is constructed using 3D image slices. Additionally video based data has an additional temporal dimension over images making it suitable for this module. \r\n\r\nImage: Lung nodule detection based on 3D convolutional neural networks, Fan et al",
"full_name": "3D Convolution",
"introduced_year": 2015,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "3D Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/key-person-aided-re-identification-in
|
1805.10017
| null | null |
Key Person Aided Re-identification in Partially Ordered Pedestrian Set
|
Ideally person re-identification seeks for perfect feature representation and
metric model that re-identify all various pedestrians well in non-overlapping
views at different locations with different camera configurations, which is
very challenging. However, in most pedestrian sets, there always are some
outstanding persons who are relatively easy to re-identify. Inspired by the
existence of such data division, we propose a novel key person aided person
re-identification framework based on the re-defined partially ordered
pedestrian sets. The outstanding persons, namely "key persons", are selected by
the K-nearest neighbor based saliency measurement. The partial order defined by
pedestrian entering time in surveillance associates the key persons with the
query person temporally and helps to locate the possible candidates.
Experiments conducted on two video datasets show that the proposed key person
aided framework outperforms the state-of-the-art methods and improves the
matching accuracy greatly at all ranks.
| null |
http://arxiv.org/abs/1805.10017v1
|
http://arxiv.org/pdf/1805.10017v1.pdf
| null |
[
"Chen Chen",
"Min Cao",
"Xiyuan Hu",
"Silong Peng"
] |
[
"Person Re-Identification"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/finite-sample-analysis-of-lstd-with-random
|
1805.10005
| null | null |
Finite Sample Analysis of LSTD with Random Projections and Eligibility Traces
|
Policy evaluation with linear function approximation is an important problem
in reinforcement learning. When facing high-dimensional feature spaces, such a
problem becomes extremely hard considering the computation efficiency and
quality of approximations. We propose a new algorithm, LSTD($\lambda$)-RP,
which leverages random projection techniques and takes eligibility traces into
consideration to tackle the above two challenges. We carry out theoretical
analysis of LSTD($\lambda$)-RP, and provide meaningful upper bounds of the
estimation error, approximation error and total generalization error. These
results demonstrate that LSTD($\lambda$)-RP can benefit from random projection
and eligibility traces strategies, and LSTD($\lambda$)-RP can achieve better
performances than prior LSTD-RP and LSTD($\lambda$) algorithms.
| null |
http://arxiv.org/abs/1805.10005v1
|
http://arxiv.org/pdf/1805.10005v1.pdf
| null |
[
"Haifang Li",
"Yingce Xia",
"Wensheng Zhang"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/masked-conditional-neural-networks-for-1
|
1805.10004
| null | null |
Masked Conditional Neural Networks for Environmental Sound Classification
|
The ConditionaL Neural Network (CLNN) exploits the nature of the temporal
sequencing of the sound signal represented in a spectrogram, and its variant
the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in
frequency bands by embedding a filterbank-like sparseness over the network's
links using a binary mask. Additionally, the masking automates the exploration
of different feature combinations concurrently analogous to handcrafting the
optimum combination of features for a recognition task. We have evaluated the
MCLNN performance using the Urbansound8k dataset of environmental sounds.
Additionally, we present a collection of manually recorded sounds for rail and
road traffic, YorNoise, to investigate the confusion rates among machine
generated sounds possessing low-frequency components. MCLNN has achieved
competitive results without augmentation and using 12% of the trainable
parameters utilized by an equivalent model based on state-of-the-art
Convolutional Neural Networks on the Urbansound8k. We extended the Urbansound8k
dataset with YorNoise, where experiments have shown that common tonal
properties affect the classification performance.
|
We have evaluated the MCLNN performance using the Urbansound8k dataset of environmental sounds.
|
http://arxiv.org/abs/1805.10004v2
|
http://arxiv.org/pdf/1805.10004v2.pdf
| null |
[
"Fady Medhat",
"David Chesmore",
"John Robinson"
] |
[
"Classification",
"Environmental Sound Classification",
"General Classification",
"Sound Classification"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-to-propagate-labels-transductive
|
1805.10002
| null |
SyVuRiC5K7
|
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
|
The goal of few-shot learning is to learn a classifier that generalizes well
even when trained with a limited number of training instances per class. The
recently introduced meta-learning approaches tackle this problem by learning a
generic classifier across a large number of multiclass classification tasks and
generalizing the model to a new task. Yet, even with such meta-learning, the
low-data problem in the novel classification task still remains. In this paper,
we propose Transductive Propagation Network (TPN), a novel meta-learning
framework for transductive inference that classifies the entire test set at
once to alleviate the low-data problem. Specifically, we propose to learn to
propagate labels from labeled instances to unlabeled test instances, by
learning a graph construction module that exploits the manifold structure in
the data. TPN jointly learns both the parameters of feature embedding and the
graph construction in an end-to-end manner. We validate TPN on multiple
benchmark datasets, on which it largely outperforms existing few-shot learning
approaches and achieves the state-of-the-art results.
|
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.
|
http://arxiv.org/abs/1805.10002v5
|
http://arxiv.org/pdf/1805.10002v5.pdf
|
ICLR 2019 5
|
[
"Yanbin Liu",
"Juho Lee",
"Minseop Park",
"Saehoon Kim",
"Eunho Yang",
"Sung Ju Hwang",
"Yi Yang"
] |
[
"Few-Shot Image Classification",
"Few-Shot Learning",
"General Classification",
"graph construction",
"Meta-Learning"
] | 2018-05-25T00:00:00 |
https://openreview.net/forum?id=SyVuRiC5K7
|
https://openreview.net/pdf?id=SyVuRiC5K7
|
learning-to-propagate-labels-transductive-1
| null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Transductive Inference",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Semi-Supervised Learning** methods leverage unlabelled data as well as labelled data to increase performance on machine learning tasks. Below you can find a continuously updating list of semi-supervised learning methods (this may have overlap with self-supervised methods due to evaluation protocol similarity).\r\n\r\n",
"name": "Semi-Supervised Learning Methods",
"parent": null
},
"name": "Transductive Inference",
"source_title": "Transductive Inference and Semi-Supervised Learning",
"source_url": "https://ieeexplore.ieee.org/abstract/document/6280886"
}
] |
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