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https://paperswithcode.com/paper/semi-supervised-learning-on-graphs-based-on
|
1802.05563
| null | null |
Semi-Supervised Learning on Graphs Based on Local Label Distributions
|
Most approaches that tackle the problem of node classification consider nodes
to be similar, if they have shared neighbors or are close to each other in the
graph. Recent methods for attributed graphs additionally take attributes of
neighboring nodes into account. We argue that the class labels of the neighbors
bear important information and considering them helps to improve classification
quality. Two nodes which are similar based on class labels in their
neighborhood do not need to be close-by in the graph and may even belong to
different connected components. In this work, we propose a novel approach for
the semi-supervised node classification. Precisely, we propose a new node
embedding which is based on the class labels in the local neighborhood of a
node. We show that this is a different setting from attribute-based embeddings
and thus, we propose a new method to learn label-based node embeddings which
can mirror a variety of relations between the class labels of neighboring
nodes. Our experimental evaluation demonstrates that our new methods can
significantly improve the prediction quality on real world data sets.
| null |
http://arxiv.org/abs/1802.05563v2
|
http://arxiv.org/pdf/1802.05563v2.pdf
| null |
[
"Evgeniy Faerman",
"Felix Borutta",
"Julian Busch",
"Matthias Schubert"
] |
[
"Attribute",
"General Classification",
"Node Classification"
] | 2018-02-15T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fully-understanding-the-hashing-trick
|
1805.08539
| null | null |
Fully Understanding the Hashing Trick
|
Feature hashing, also known as {\em the hashing trick}, introduced by
Weinberger et al. (2009), is one of the key techniques used in scaling-up
machine learning algorithms. Loosely speaking, feature hashing uses a random
sparse projection matrix $A : \mathbb{R}^n \to \mathbb{R}^m$ (where $m \ll n$)
in order to reduce the dimension of the data from $n$ to $m$ while
approximately preserving the Euclidean norm. Every column of $A$ contains
exactly one non-zero entry, equals to either $-1$ or $1$.
Weinberger et al. showed tail bounds on $\|Ax\|_2^2$. Specifically they
showed that for every $\varepsilon, \delta$, if $\|x\|_{\infty} / \|x\|_2$ is
sufficiently small, and $m$ is sufficiently large, then $$\Pr[ \; |
\;\|Ax\|_2^2 - \|x\|_2^2\; | < \varepsilon \|x\|_2^2 \;] \ge 1 - \delta \;.$$
These bounds were later extended by Dasgupta \etal (2010) and most recently
refined by Dahlgaard et al. (2017), however, the true nature of the performance
of this key technique, and specifically the correct tradeoff between the
pivotal parameters $\|x\|_{\infty} / \|x\|_2, m, \varepsilon, \delta$ remained
an open question.
We settle this question by giving tight asymptotic bounds on the exact
tradeoff between the central parameters, thus providing a complete
understanding of the performance of feature hashing. We complement the
asymptotic bound with empirical data, which shows that the constants "hiding"
in the asymptotic notation are, in fact, very close to $1$, thus further
illustrating the tightness of the presented bounds in practice.
| null |
http://arxiv.org/abs/1805.08539v1
|
http://arxiv.org/pdf/1805.08539v1.pdf
|
NeurIPS 2018 12
|
[
"Casper Benjamin Freksen",
"Lior Kamma",
"Kasper Green Larsen"
] |
[
"Open-Ended Question Answering"
] | 2018-05-22T00:00:00 |
http://papers.nips.cc/paper/7784-fully-understanding-the-hashing-trick
|
http://papers.nips.cc/paper/7784-fully-understanding-the-hashing-trick.pdf
|
fully-understanding-the-hashing-trick-1
| null |
[] |
https://paperswithcode.com/paper/first-hitting-times-under-additive-drift
|
1805.09415
| null | null |
First-Hitting Times Under Additive Drift
|
For the last ten years, almost every theoretical result concerning the
expected run time of a randomized search heuristic used drift theory, making it
the arguably most important tool in this domain. Its success is due to its ease
of use and its powerful result: drift theory allows the user to derive bounds
on the expected first-hitting time of a random process by bounding expected
local changes of the process -- the drift. This is usually far easier than
bounding the expected first-hitting time directly.
Due to the widespread use of drift theory, it is of utmost importance to have
the best drift theorems possible. We improve the fundamental additive,
multiplicative, and variable drift theorems by stating them in a form as
general as possible and providing examples of why the restrictions we keep are
still necessary. Our additive drift theorem for upper bounds only requires the
process to be nonnegative, that is, we remove unnecessary restrictions like a
finite, discrete, or bounded search space. As corollaries, the same is true for
our upper bounds in the case of variable and multiplicative drift.
| null |
http://arxiv.org/abs/1805.09415v1
|
http://arxiv.org/pdf/1805.09415v1.pdf
| null |
[
"Timo Kötzing",
"Martin S. Krejca"
] |
[] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/sentiment-analysis-of-arabic-tweets-feature
|
1805.08533
| null | null |
Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach
|
Sentiment Analysis in Arabic is a challenging task due to the rich morphology
of the language. Moreover, the task is further complicated when applied to
Twitter data that is known to be highly informal and noisy. In this paper, we
develop a hybrid method for sentiment analysis for Arabic tweets for a specific
Arabic dialect which is the Saudi Dialect. Several features were engineered and
evaluated using a feature backward selection method. Then a hybrid method that
combines a corpus-based and lexicon-based method was developed for several
classification models (two-way, three-way, four-way). The best F1-score for
each of these models was (69.9,61.63,55.07) respectively.
| null |
http://arxiv.org/abs/1805.08533v1
|
http://arxiv.org/pdf/1805.08533v1.pdf
| null |
[
"Nora Al-Twairesh",
"Hend Al-Khalifa",
"Abdulmalik Al-Salman",
"Yousef Al-Ohali"
] |
[
"Feature Engineering",
"General Classification",
"Sentiment Analysis"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-local-descriptors-by-optimizing-the
|
1603.09095
| null | null |
Learning Local Descriptors by Optimizing the Keypoint-Correspondence Criterion: Applications to Face Matching, Learning from Unlabeled Videos and 3D-Shape Retrieval
|
Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs. However, data of this kind is not always available since detailed keypoint correspondences can be hard to establish. On the other hand, we can often obtain labels for pairs of keypoint bags. For example, keypoint bags extracted from two images of the same object under different views form a matching pair, and keypoint bags extracted from images of different objects form a non-matching pair. On average, matching pairs should contain more corresponding keypoints than non-matching pairs. We describe an end-to-end differentiable architecture that enables the learning of local keypoint descriptors from such weakly-labeled data. Additionally, we discuss how to improve the method by incorporating the procedure of mining hard negatives. We also show how can our approach be used to learn convolutional features from unlabeled video signals and 3D models. Our implementation is available at https://github.com/nenadmarkus/wlrn
|
Current best local descriptors are learned on a large dataset of matching and non-matching keypoint pairs.
|
https://arxiv.org/abs/1603.09095v6
|
https://arxiv.org/pdf/1603.09095v6.pdf
| null |
[
"Nenad Markuš",
"Igor S. Pandžić",
"Jörgen Ahlberg"
] |
[
"3D Shape Classification",
"3D Shape Retrieval",
"Retrieval"
] | 2016-03-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/accelerated-gossip-in-networks-of-given
|
1805.08531
| null | null |
Accelerated Gossip in Networks of Given Dimension using Jacobi Polynomial Iterations
|
Consider a network of agents connected by communication links, where each agent holds a real value. The gossip problem consists in estimating the average of the values diffused in the network in a distributed manner. We develop a method solving the gossip problem that depends only on the spectral dimension of the network, that is, in the communication network set-up, the dimension of the space in which the agents live. This contrasts with previous work that required the spectral gap of the network as a parameter, or suffered from slow mixing. Our method shows an important improvement over existing algorithms in the non-asymptotic regime, i.e., when the values are far from being fully mixed in the network. Our approach stems from a polynomial-based point of view on gossip algorithms, as well as an approximation of the spectral measure of the graphs with a Jacobi measure. We show the power of the approach with simulations on various graphs, and with performance guarantees on graphs of known spectral dimension, such as grids and random percolation bonds. An extension of this work to distributed Laplacian solvers is discussed. As a side result, we also use the polynomial-based point of view to show the convergence of the message passing algorithm for gossip of Moallemi \& Van Roy on regular graphs. The explicit computation of the rate of the convergence shows that message passing has a slow rate of convergence on graphs with small spectral gap.
|
We develop a method solving the gossip problem that depends only on the spectral dimension of the network, that is, in the communication network set-up, the dimension of the space in which the agents live.
|
https://arxiv.org/abs/1805.08531v4
|
https://arxiv.org/pdf/1805.08531v4.pdf
| null |
[
"Raphaël Berthier",
"Francis Bach",
"Pierre Gaillard"
] |
[
"Denoising"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/recognizing-objects-in-the-wild-where-do-we
|
1709.05862
| null | null |
Recognizing Objects In-the-wild: Where Do We Stand?
|
The ability to recognize objects is an essential skill for a robotic system
acting in human-populated environments. Despite decades of effort from the
robotic and vision research communities, robots are still missing good visual
perceptual systems, preventing the use of autonomous agents for real-world
applications. The progress is slowed down by the lack of a testbed able to
accurately represent the world perceived by the robot in-the-wild. In order to
fill this gap, we introduce a large-scale, multi-view object dataset collected
with an RGB-D camera mounted on a mobile robot. The dataset embeds the
challenges faced by a robot in a real-life application and provides a useful
tool for validating object recognition algorithms. Besides describing the
characteristics of the dataset, the paper evaluates the performance of a
collection of well-established deep convolutional networks on the new dataset
and analyzes the transferability of deep representations from Web images to
robotic data. Despite the promising results obtained with such representations,
the experiments demonstrate that object classification with real-life robotic
data is far from being solved. Finally, we provide a comparative study to
analyze and highlight the open challenges in robot vision, explaining the
discrepancies in the performance.
| null |
http://arxiv.org/abs/1709.05862v2
|
http://arxiv.org/pdf/1709.05862v2.pdf
| null |
[
"Mohammad Reza Loghmani",
"Barbara Caputo",
"Markus Vincze"
] |
[
"Object",
"Object Recognition"
] | 2017-09-18T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/independently-recurrent-neural-network-indrnn
|
1803.04831
| null | null |
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
|
Recurrent neural networks (RNNs) have been widely used for processing
sequential data. However, RNNs are commonly difficult to train due to the
well-known gradient vanishing and exploding problems and hard to learn
long-term patterns. Long short-term memory (LSTM) and gated recurrent unit
(GRU) were developed to address these problems, but the use of hyperbolic
tangent and the sigmoid action functions results in gradient decay over layers.
Consequently, construction of an efficiently trainable deep network is
challenging. In addition, all the neurons in an RNN layer are entangled
together and their behaviour is hard to interpret. To address these problems, a
new type of RNN, referred to as independently recurrent neural network
(IndRNN), is proposed in this paper, where neurons in the same layer are
independent of each other and they are connected across layers. We have shown
that an IndRNN can be easily regulated to prevent the gradient exploding and
vanishing problems while allowing the network to learn long-term dependencies.
Moreover, an IndRNN can work with non-saturated activation functions such as
relu (rectified linear unit) and be still trained robustly. Multiple IndRNNs
can be stacked to construct a network that is deeper than the existing RNNs.
Experimental results have shown that the proposed IndRNN is able to process
very long sequences (over 5000 time steps), can be used to construct very deep
networks (21 layers used in the experiment) and still be trained robustly.
Better performances have been achieved on various tasks by using IndRNNs
compared with the traditional RNN and LSTM. The code is available at
https://github.com/Sunnydreamrain/IndRNN_Theano_Lasagne.
|
Experimental results have shown that the proposed IndRNN is able to process very long sequences (over 5000 time steps), can be used to construct very deep networks (21 layers used in the experiment) and still be trained robustly.
|
http://arxiv.org/abs/1803.04831v3
|
http://arxiv.org/pdf/1803.04831v3.pdf
|
CVPR 2018 6
|
[
"Shuai Li",
"Wanqing Li",
"Chris Cook",
"Ce Zhu",
"Yanbo Gao"
] |
[
"Language Modelling",
"Sequential Image Classification",
"Skeleton Based Action Recognition"
] | 2018-03-13T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Independently_Recurrent_Neural_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Independently_Recurrent_Neural_CVPR_2018_paper.pdf
|
independently-recurrent-neural-network-indrnn-1
| 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/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": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/ok-google-what-is-your-ontology-or-exploring
|
1805.03885
| null | null |
OK Google, What Is Your Ontology? Or: Exploring Freebase Classification to Understand Google's Knowledge Graph
|
This paper reconstructs the Freebase data dumps to understand the underlying
ontology behind Google's semantic search feature. The Freebase knowledge base
was a major Semantic Web and linked data technology that was acquired by Google
in 2010 to support the Google Knowledge Graph, the backend for Google search
results that include structured answers to queries instead of a series of links
to external resources. After its shutdown in 2016, Freebase is contained in a
data dump of 1.9 billion Resource Description Format (RDF) triples. A
recomposition of the Freebase ontology will be analyzed in relation to concepts
and insights from the literature on classification by Bowker and Star. This
paper will explore how the Freebase ontology is shaped by many of the forces
that also shape classification systems through a deep dive into the ontology
and a small correlational study. These findings will provide a glimpse into the
proprietary blackbox Knowledge Graph and what is meant by Google's mission to
"organize the world's information and make it universally accessible and
useful".
| null |
http://arxiv.org/abs/1805.03885v2
|
http://arxiv.org/pdf/1805.03885v2.pdf
| null |
[
"Niel Chah"
] |
[
"General Classification"
] | 2018-05-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/automated-cardiovascular-magnetic-resonance
|
1710.09289
| null | null |
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks
|
Cardiovascular magnetic resonance (CMR) imaging is a standard imaging
modality for assessing cardiovascular diseases (CVDs), the leading cause of
death globally. CMR enables accurate quantification of the cardiac chamber
volume, ejection fraction and myocardial mass, providing information for
diagnosis and monitoring of CVDs. However, for years, clinicians have been
relying on manual approaches for CMR image analysis, which is time consuming
and prone to subjective errors. It is a major clinical challenge to
automatically derive quantitative and clinically relevant information from CMR
images. Deep neural networks have shown a great potential in image pattern
recognition and segmentation for a variety of tasks. Here we demonstrate an
automated analysis method for CMR images, which is based on a fully
convolutional network (FCN). The network is trained and evaluated on a
large-scale dataset from the UK Biobank, consisting of 4,875 subjects with
93,500 pixelwise annotated images. The performance of the method has been
evaluated using a number of technical metrics, including the Dice metric, mean
contour distance and Hausdorff distance, as well as clinically relevant
measures, including left ventricle (LV) end-diastolic volume (LVEDV) and
end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic
volume (RVEDV) and end-systolic volume (RVESV). By combining FCN with a
large-scale annotated dataset, the proposed automated method achieves a high
performance on par with human experts in segmenting the LV and RV on short-axis
CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR
images.
|
By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images.
|
http://arxiv.org/abs/1710.09289v4
|
http://arxiv.org/pdf/1710.09289v4.pdf
| null |
[
"Wenjia Bai",
"Matthew Sinclair",
"Giacomo Tarroni",
"Ozan Oktay",
"Martin Rajchl",
"Ghislain Vaillant",
"Aaron M. Lee",
"Nay Aung",
"Elena Lukaschuk",
"Mihir M. Sanghvi",
"Filip Zemrak",
"Kenneth Fung",
"Jose Miguel Paiva",
"Valentina Carapella",
"Young Jin Kim",
"Hideaki Suzuki",
"Bernhard Kainz",
"Paul M. Matthews",
"Steffen E. Petersen",
"Stefan K. Piechnik",
"Stefan Neubauer",
"Ben Glocker",
"Daniel Rueckert"
] |
[] | 2017-10-25T00:00:00 | null | null | null | 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": "",
"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/Jackey9797/FCN",
"description": "**Fully Convolutional Networks**, or **FCNs**, are an architecture used mainly for semantic segmentation. They employ solely locally connected layers, such as [convolution](https://paperswithcode.com/method/convolution), pooling and upsampling. Avoiding the use of dense layers means less parameters (making the networks faster to train). It also means an FCN can work for variable image sizes given all connections are local.\r\n\r\nThe network consists of a downsampling path, used to extract and interpret the context, and an upsampling path, which allows for localization. \r\n\r\nFCNs also employ skip connections to recover the fine-grained spatial information lost in the downsampling path.",
"full_name": "Fully Convolutional Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Semantic Segmentation Models** are a class of methods that address the task of semantically segmenting an image into different object classes. Below you can find a continuously updating list of semantic segmentation models. ",
"name": "Semantic Segmentation Models",
"parent": null
},
"name": "FCN",
"source_title": "Fully Convolutional Networks for Semantic Segmentation",
"source_url": "http://arxiv.org/abs/1605.06211v1"
}
] |
https://paperswithcode.com/paper/part-based-tracking-by-sampling
|
1805.08511
| null | null |
Part-based Tracking by Sampling
|
We propose a novel part-based method for tracking an arbitrary object in challenging video sequences. The colour distribution of tracked image patches on the target object are represented by pairs of RGB samples and counts of how many pixels in the patch are similar to them. Patches are placed by segmenting the object in the given bounding box and placing patches in homogeneous regions of the object. These are located in subsequent image frames by applying non-shearing affine transformations to the patches' previous locations, locally optimising the best of these, and evaluating their quality using a modified Bhattacharyya distance. In experiments carried out on VOT2018 and OTB100 benchmarks, the tracker achieves higher performance than all other part-based trackers. An ablation study is used to reveal the effectiveness of each tracking component, with largest performance gains found when using the patch placement scheme.
| null |
https://arxiv.org/abs/1805.08511v2
|
https://arxiv.org/pdf/1805.08511v2.pdf
| null |
[
"George De Ath",
"Richard M. Everson"
] |
[
"Object"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/optimal-approximation-of-piecewise-smooth
|
1709.05289
| null | null |
Optimal approximation of piecewise smooth functions using deep ReLU neural networks
|
We study the necessary and sufficient complexity of ReLU neural networks---in
terms of depth and number of weights---which is required for approximating
classifier functions in $L^2$. As a model class, we consider the set
$\mathcal{E}^\beta (\mathbb R^d)$ of possibly discontinuous piecewise $C^\beta$
functions $f : [-1/2, 1/2]^d \to \mathbb R$, where the different smooth regions
of $f$ are separated by $C^\beta$ hypersurfaces. For dimension $d \geq 2$,
regularity $\beta > 0$, and accuracy $\varepsilon > 0$, we construct artificial
neural networks with ReLU activation function that approximate functions from
$\mathcal{E}^\beta(\mathbb R^d)$ up to $L^2$ error of $\varepsilon$. The
constructed networks have a fixed number of layers, depending only on $d$ and
$\beta$, and they have $O(\varepsilon^{-2(d-1)/\beta})$ many nonzero weights,
which we prove to be optimal. In addition to the optimality in terms of the
number of weights, we show that in order to achieve the optimal approximation
rate, one needs ReLU networks of a certain depth. Precisely, for piecewise
$C^\beta(\mathbb R^d)$ functions, this minimal depth is given---up to a
multiplicative constant---by $\beta/d$. Up to a log factor, our constructed
networks match this bound. This partly explains the benefits of depth for ReLU
networks by showing that deep networks are necessary to achieve efficient
approximation of (piecewise) smooth functions. Finally, we analyze
approximation in high-dimensional spaces where the function $f$ to be
approximated can be factorized into a smooth dimension reducing feature map
$\tau$ and classifier function $g$---defined on a low-dimensional feature
space---as $f = g \circ \tau$. We show that in this case the approximation rate
depends only on the dimension of the feature space and not the input dimension.
| null |
http://arxiv.org/abs/1709.05289v4
|
http://arxiv.org/pdf/1709.05289v4.pdf
| null |
[
"Philipp Petersen",
"Felix Voigtlaender"
] |
[] | 2017-09-15T00:00:00 | null | null | null | null |
[
{
"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
}
] |
https://paperswithcode.com/paper/learning-parametric-closed-loop-policies-for
|
1802.00899
| null |
rJm7VfZA-
|
Learning Parametric Closed-Loop Policies for Markov Potential Games
|
Multiagent systems where agents interact among themselves and with a
stochastic environment can be formalized as stochastic games. We study a
subclass named Markov potential games (MPGs) that appear often in economic and
engineering applications when the agents share a common resource. We consider
MPGs with continuous state-action variables, coupled constraints and nonconvex
rewards. Previous analysis followed a variational approach that is only valid
for very simple cases (convex rewards, invertible dynamics, and no coupled
constraints); or considered deterministic dynamics and provided open-loop (OL)
analysis, studying strategies that consist in predefined action sequences,
which are not optimal for stochastic environments. We present a closed-loop
(CL) analysis for MPGs and consider parametric policies that depend on the
current state. We provide easily verifiable, sufficient and necessary
conditions for a stochastic game to be an MPG, even for complex parametric
functions (e.g., deep neural networks); and show that a closed-loop Nash
equilibrium (NE) can be found (or at least approximated) by solving a related
optimal control problem (OCP). This is useful since solving an OCP--which is a
single-objective problem--is usually much simpler than solving the original set
of coupled OCPs that form the game--which is a multiobjective control problem.
This is a considerable improvement over the previously standard approach for
the CL analysis of MPGs, which gives no approximate solution if no NE belongs
to the chosen parametric family, and which is practical only for simple
parametric forms. We illustrate the theoretical contributions with an example
by applying our approach to a noncooperative communications engineering game.
We then solve the game with a deep reinforcement learning algorithm that learns
policies that closely approximates an exact variational NE of the game.
| null |
http://arxiv.org/abs/1802.00899v2
|
http://arxiv.org/pdf/1802.00899v2.pdf
|
ICLR 2018 1
|
[
"Sergio Valcarcel Macua",
"Javier Zazo",
"Santiago Zazo"
] |
[
"Deep Reinforcement Learning",
"Reinforcement Learning"
] | 2018-02-03T00:00:00 |
https://openreview.net/forum?id=rJm7VfZA-
|
https://openreview.net/pdf?id=rJm7VfZA-
|
learning-parametric-closed-loop-policies-for-1
| null |
[] |
https://paperswithcode.com/paper/improved-person-detection-on-omnidirectional
|
1805.08503
| null | null |
Improved Person Detection on Omnidirectional Images with Non-maxima Suppression
|
We propose a person detector on omnidirectional images, an accurate method to
generate minimal enclosing rectangles of persons. The basic idea is to adapt
the qualitative detection performance of a convolutional neural network based
method, namely YOLOv2 to fish-eye images. The design of our approach picks up
the idea of a state-of-the-art object detector and highly overlapping areas of
images with their regions of interests. This overlap reduces the number of
false negatives. Based on the raw bounding boxes of the detector we fine-tuned
overlapping bounding boxes by three approaches: non-maximum suppression, soft
non-maximum suppression and soft non-maximum suppression with Gaussian
smoothing. The evaluation was done on the PIROPO database and an own annotated
Flat dataset, supplemented with bounding boxes on omnidirectional images. We
achieve an average precision of 64.4 % with YOLOv2 for the class person on
PIROPO and 77.6 % on Flat. For this purpose we fine-tuned the soft non-maximum
suppression with Gaussian smoothing.
| null |
http://arxiv.org/abs/1805.08503v4
|
http://arxiv.org/pdf/1805.08503v4.pdf
| null |
[
"Roman Seidel",
"André Apitzsch",
"Gangolf Hirtz"
] |
[
"Human Detection"
] | 2018-05-22T00:00:00 | 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/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": "",
"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": 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": "",
"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/longcw/yolo2-pytorch/blob/17056ca69f097a07884135d9031c53d4ef217a6a/darknet.py#L140",
"description": "**Darknet-19** is a convolutional neural network that is used as the backbone of [YOLOv2](https://paperswithcode.com/method/yolov2). Similar to the [VGG](https://paperswithcode.com/method/vgg) models it mostly uses $3 \\times 3$ filters and doubles the number of channels after every pooling step. Following the work on Network in Network (NIN) it uses [global average pooling](https://paperswithcode.com/method/global-average-pooling) to make predictions as well as $1 \\times 1$ filters to compress the feature representation between $3 \\times 3$ convolutions. [Batch Normalization](https://paperswithcode.com/method/batch-normalization) is used to stabilize training, speed up convergence, and regularize the model batch.",
"full_name": "Darknet-19",
"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": "Darknet-19",
"source_title": "YOLO9000: Better, Faster, Stronger",
"source_url": "http://arxiv.org/abs/1612.08242v1"
},
{
"code_snippet_url": "https://github.com/pjreddie/darknet",
"description": "**YOLOv2**, or [**YOLO9000**](https://www.youtube.com/watch?v=QsDDXSmGJZA), is a single-stage real-time object detection model. It improves upon [YOLOv1](https://paperswithcode.com/method/yolov1) in several ways, including the use of [Darknet-19](https://paperswithcode.com/method/darknet-19) as a backbone, [batch normalization](https://paperswithcode.com/method/batch-normalization), use of a high-resolution classifier, and the use of anchor boxes to predict bounding boxes, and more.",
"full_name": "YOLOv2",
"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": "YOLOv2",
"source_title": "YOLO9000: Better, Faster, Stronger",
"source_url": "http://arxiv.org/abs/1612.08242v1"
}
] |
https://paperswithcode.com/paper/blind-predicting-similar-quality-map-for
|
1805.08493
| null | null |
Blind Predicting Similar Quality Map for Image Quality Assessment
|
A key problem in blind image quality assessment (BIQA) is how to effectively
model the properties of human visual system in a data-driven manner. In this
paper, we propose a simple and efficient BIQA model based on a novel framework
which consists of a fully convolutional neural network (FCNN) and a pooling
network to solve this problem. In principle, FCNN is capable of predicting a
pixel-by-pixel similar quality map only from a distorted image by using the
intermediate similarity maps derived from conventional full-reference image
quality assessment methods. The predicted pixel-by-pixel quality maps have good
consistency with the distortion correlations between the reference and
distorted images. Finally, a deep pooling network regresses the quality map
into a score. Experiments have demonstrated that our predictions outperform
many state-of-the-art BIQA methods.
| null |
http://arxiv.org/abs/1805.08493v2
|
http://arxiv.org/pdf/1805.08493v2.pdf
|
CVPR 2018 6
|
[
"Da Pan",
"Ping Shi",
"Ming Hou",
"Zefeng Ying",
"Sizhe Fu",
"Yuan Zhang"
] |
[
"Blind Image Quality Assessment",
"Full reference image quality assessment",
"Full-Reference Image Quality Assessment",
"Image Quality Assessment"
] | 2018-05-22T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Pan_Blind_Predicting_Similar_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Pan_Blind_Predicting_Similar_CVPR_2018_paper.pdf
|
blind-predicting-similar-quality-map-for-1
| null |
[] |
https://paperswithcode.com/paper/policy-gradients-for-contextual
|
1802.04162
| null | null |
Policy Gradients for Contextual Recommendations
|
Decision making is a challenging task in online recommender systems. The
decision maker often needs to choose a contextual item at each step from a set
of candidates. Contextual bandit algorithms have been successfully deployed to
such applications, for the trade-off between exploration and exploitation and
the state-of-art performance on minimizing online costs. However, the
applicability of existing contextual bandit methods is limited by the
over-simplified assumptions of the problem, such as assuming a simple form of
the reward function or assuming a static environment where the states are not
affected by previous actions. In this work, we put forward Policy Gradients for
Contextual Recommendations (PGCR) to solve the problem without those
unrealistic assumptions. It optimizes over a restricted class of policies where
the marginal probability of choosing an item (in expectation of other items)
has a simple closed form, and the gradient of the expected return over the
policy in this class is in a succinct form. Moreover, PGCR leverages two useful
heuristic techniques called Time-Dependent Greed and Actor-Dropout. The former
ensures PGCR to be empirically greedy in the limit, and the latter addresses
the trade-off between exploration and exploitation by using the policy network
with Dropout as a Bayesian approximation. PGCR can solve the standard
contextual bandits as well as its Markov Decision Process generalization.
Therefore it can be applied to a wide range of realistic settings of
recommendations, such as personalized advertising. We evaluate PGCR on toy
datasets as well as a real-world dataset of personalized music recommendations.
Experiments show that PGCR enables fast convergence and low regret, and
outperforms both classic contextual-bandits and vanilla policy gradient
methods.
| null |
http://arxiv.org/abs/1802.04162v3
|
http://arxiv.org/pdf/1802.04162v3.pdf
| null |
[
"Feiyang Pan",
"Qingpeng Cai",
"Pingzhong Tang",
"Fuzhen Zhuang",
"Qing He"
] |
[
"Decision Making",
"Multi-Armed Bandits",
"Policy Gradient Methods",
"Recommendation Systems"
] | 2018-02-12T00:00:00 | null | null | null | 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"
}
] |
https://paperswithcode.com/paper/knowledge-based-fully-convolutional-network
|
1805.08492
| null | null |
Knowledge-based Fully Convolutional Network and Its Application in Segmentation of Lung CT Images
|
A variety of deep neural networks have been applied in medical image
segmentation and achieve good performance. Unlike natural images, medical
images of the same imaging modality are characterized by the same pattern,
which indicates that same normal organs or tissues locate at similar positions
in the images. Thus, in this paper we try to incorporate the prior knowledge of
medical images into the structure of neural networks such that the prior
knowledge can be utilized for accurate segmentation. Based on this idea, we
propose a novel deep network called knowledge-based fully convolutional network
(KFCN) for medical image segmentation. The segmentation function and
corresponding error is analyzed. We show the existence of an asymptotically
stable region for KFCN which traditional FCN doesn't possess. Experiments
validate our knowledge assumption about the incorporation of prior knowledge
into the convolution kernels of KFCN and show that KFCN can achieve a
reasonable segmentation and a satisfactory accuracy.
| null |
http://arxiv.org/abs/1805.08492v1
|
http://arxiv.org/pdf/1805.08492v1.pdf
| null |
[
"Tao Yu",
"Yu Qiao",
"Huan Long"
] |
[
"Image Segmentation",
"Medical Image Segmentation",
"Segmentation",
"Semantic Segmentation"
] | 2018-05-22T00:00:00 | null | null | null | 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": "",
"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/Jackey9797/FCN",
"description": "**Fully Convolutional Networks**, or **FCNs**, are an architecture used mainly for semantic segmentation. They employ solely locally connected layers, such as [convolution](https://paperswithcode.com/method/convolution), pooling and upsampling. Avoiding the use of dense layers means less parameters (making the networks faster to train). It also means an FCN can work for variable image sizes given all connections are local.\r\n\r\nThe network consists of a downsampling path, used to extract and interpret the context, and an upsampling path, which allows for localization. \r\n\r\nFCNs also employ skip connections to recover the fine-grained spatial information lost in the downsampling path.",
"full_name": "Fully Convolutional Network",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Semantic Segmentation Models** are a class of methods that address the task of semantically segmenting an image into different object classes. Below you can find a continuously updating list of semantic segmentation models. ",
"name": "Semantic Segmentation Models",
"parent": null
},
"name": "FCN",
"source_title": "Fully Convolutional Networks for Semantic Segmentation",
"source_url": "http://arxiv.org/abs/1605.06211v1"
}
] |
https://paperswithcode.com/paper/general-bayesian-updating-and-the-loss
|
1709.07616
| null | null |
General Bayesian Updating and the Loss-Likelihood Bootstrap
|
In this paper we revisit the weighted likelihood bootstrap, a method that
generates samples from an approximate Bayesian posterior of a parametric model.
We show that the same method can be derived, without approximation, under a
Bayesian nonparametric model with the parameter of interest defined as
minimising an expected negative log-likelihood under an unknown sampling
distribution. This interpretation enables us to extend the weighted likelihood
bootstrap to posterior sampling for parameters minimizing an expected loss. We
call this method the loss-likelihood bootstrap. We make a connection between
this and general Bayesian updating, which is a way of updating prior belief
distributions without needing to construct a global probability model, yet
requires the calibration of two forms of loss function. The loss-likelihood
bootstrap is used to calibrate the general Bayesian posterior by matching
asymptotic Fisher information. We demonstrate the methodology on a number of
examples.
| null |
http://arxiv.org/abs/1709.07616v2
|
http://arxiv.org/pdf/1709.07616v2.pdf
| null |
[
"Simon Lyddon",
"Chris Holmes",
"Stephen Walker"
] |
[] | 2017-09-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/generative-code-modeling-with-graphs
|
1805.08490
| null |
Bke4KsA5FX
|
Generative Code Modeling with Graphs
|
Generative models for source code are an interesting structured prediction
problem, requiring to reason about both hard syntactic and semantic constraints
as well as about natural, likely programs. We present a novel model for this
problem that uses a graph to represent the intermediate state of the generated
output. The generative procedure interleaves grammar-driven expansion steps
with graph augmentation and neural message passing steps. An experimental
evaluation shows that our new model can generate semantically meaningful
expressions, outperforming a range of strong baselines.
|
Generative models for source code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs.
|
http://arxiv.org/abs/1805.08490v2
|
http://arxiv.org/pdf/1805.08490v2.pdf
|
ICLR 2019 5
|
[
"Marc Brockschmidt",
"Miltiadis Allamanis",
"Alexander L. Gaunt",
"Oleksandr Polozov"
] |
[
"Structured Prediction"
] | 2018-05-22T00:00:00 |
https://openreview.net/forum?id=Bke4KsA5FX
|
https://openreview.net/pdf?id=Bke4KsA5FX
|
generative-code-modeling-with-graphs-1
| null |
[] |
https://paperswithcode.com/paper/a-unified-view-of-piecewise-linear-neural
|
1711.00455
| null | null |
A Unified View of Piecewise Linear Neural Network Verification
|
The success of Deep Learning and its potential use in many safety-critical
applications has motivated research on formal verification of Neural Network
(NN) models. Despite the reputation of learned NN models to behave as black
boxes and the theoretical hardness of proving their properties, researchers
have been successful in verifying some classes of models by exploiting their
piecewise linear structure and taking insights from formal methods such as
Satisifiability Modulo Theory. These methods are however still far from scaling
to realistic neural networks. To facilitate progress on this crucial area, we
make two key contributions. First, we present a unified framework that
encompasses previous methods. This analysis results in the identification of
new methods that combine the strengths of multiple existing approaches,
accomplishing a speedup of two orders of magnitude compared to the previous
state of the art. Second, we propose a new data set of benchmarks which
includes a collection of previously released testcases. We use the benchmark to
provide the first experimental comparison of existing algorithms and identify
the factors impacting the hardness of verification problems.
|
The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models.
|
http://arxiv.org/abs/1711.00455v3
|
http://arxiv.org/pdf/1711.00455v3.pdf
|
NeurIPS 2018 12
|
[
"Rudy Bunel",
"Ilker Turkaslan",
"Philip H. S. Torr",
"Pushmeet Kohli",
"M. Pawan Kumar"
] |
[] | 2017-11-01T00:00:00 |
http://papers.nips.cc/paper/7728-a-unified-view-of-piecewise-linear-neural-network-verification
|
http://papers.nips.cc/paper/7728-a-unified-view-of-piecewise-linear-neural-network-verification.pdf
|
a-unified-view-of-piecewise-linear-neural-1
| null |
[] |
https://paperswithcode.com/paper/pose-based-two-stream-relational-networks-for
|
1805.08484
| null | null |
Pose-Based Two-Stream Relational Networks for Action Recognition in Videos
|
Recently, pose-based action recognition has gained more and more attention
due to the better performance compared with traditional appearance-based
methods. However, there still exist two problems to be further solved. First,
existing pose-based methods generally recognize human actions with captured 3D
human poses which are very difficult to obtain in real scenarios. Second, few
pose-based methods model the action-related objects in recognizing human-object
interaction actions in which objects play an important role. To solve the
problems above, we propose a pose-based two-stream relational network (PSRN)
for action recognition. In PSRN, one stream models the temporal dynamics of the
targeted 2D human pose sequences which are directly extracted from raw videos,
and the other stream models the action-related objects from a randomly sampled
video frame. Most importantly, instead of fusing two-streams in the class score
layer as before, we propose a pose-object relational network to model the
relationship between human poses and action-related objects. We evaluate the
proposed PSRN on two challenging benchmarks, i.e., Sub-JHMDB and PennAction.
Experimental results show that our PSRN obtains the state-the-of-art
performance on Sub-JHMDB (80.2%) and PennAction (98.1%). Our work opens a new
door to action recognition by combining 2D human pose extracted from raw video
and image appearance.
| null |
http://arxiv.org/abs/1805.08484v1
|
http://arxiv.org/pdf/1805.08484v1.pdf
| null |
[
"Wei Wang",
"Jinjin Zhang",
"Chenyang Si",
"Liang Wang"
] |
[
"Action Recognition",
"Action Recognition In Videos",
"Human-Object Interaction Detection",
"Temporal Action Localization",
"Vocal Bursts Valence Prediction"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/the-topology-toolkit
|
1805.09110
| null | null |
The Topology ToolKit
|
This system paper presents the Topology ToolKit (TTK), a software platform designed for topological data analysis in scientific visualization. TTK provides a unified, generic, efficient, and robust implementation of key algorithms for the topological analysis of scalar data, including: critical points, integral lines, persistence diagrams, persistence curves, merge trees, contour trees, Morse-Smale complexes, fiber surfaces, continuous scatterplots, Jacobi sets, Reeb spaces, and more. TTK is easily accessible to end users due to a tight integration with ParaView. It is also easily accessible to developers through a variety of bindings (Python, VTK/C++) for fast prototyping or through direct, dependence-free, C++, to ease integration into pre-existing complex systems. While developing TTK, we faced several algorithmic and software engineering challenges, which we document in this paper. In particular, we present an algorithm for the construction of a discrete gradient that complies to the critical points extracted in the piecewise-linear setting. This algorithm guarantees a combinatorial consistency across the topological abstractions supported by TTK, and importantly, a unified implementation of topological data simplification for multi-scale exploration and analysis. We also present a cached triangulation data structure, that supports time efficient and generic traversals, which self-adjusts its memory usage on demand for input simplicial meshes and which implicitly emulates a triangulation for regular grids with no memory overhead. Finally, we describe an original software architecture, which guarantees memory efficient and direct accesses to TTK features, while still allowing for researchers powerful and easy bindings and extensions. TTK is open source (BSD license) and its code, online documentation and video tutorials are available on TTK's website.
| null |
https://arxiv.org/abs/1805.09110v2
|
https://arxiv.org/pdf/1805.09110v2.pdf
| null |
[
"Julien Tierny",
"Guillaume Favelier",
"Joshua A. Levine",
"Charles Gueunet",
"Michael Michaux"
] |
[
"Topological Data Analysis"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/leveraging-grammar-and-reinforcement-learning
|
1805.04276
| null |
H1Xw62kRZ
|
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
|
Program synthesis is the task of automatically generating a program
consistent with a specification. Recent years have seen proposal of a number of
neural approaches for program synthesis, many of which adopt a sequence
generation paradigm similar to neural machine translation, in which
sequence-to-sequence models are trained to maximize the likelihood of known
reference programs. While achieving impressive results, this strategy has two
key limitations. First, it ignores Program Aliasing: the fact that many
different programs may satisfy a given specification (especially with
incomplete specifications such as a few input-output examples). By maximizing
the likelihood of only a single reference program, it penalizes many
semantically correct programs, which can adversely affect the synthesizer
performance. Second, this strategy overlooks the fact that programs have a
strict syntax that can be efficiently checked. To address the first limitation,
we perform reinforcement learning on top of a supervised model with an
objective that explicitly maximizes the likelihood of generating semantically
correct programs. For addressing the second limitation, we introduce a training
procedure that directly maximizes the probability of generating syntactically
correct programs that fulfill the specification. We show that our contributions
lead to improved accuracy of the models, especially in cases where the training
data is limited.
| null |
http://arxiv.org/abs/1805.04276v2
|
http://arxiv.org/pdf/1805.04276v2.pdf
|
ICLR 2018 1
|
[
"Rudy Bunel",
"Matthew Hausknecht",
"Jacob Devlin",
"Rishabh Singh",
"Pushmeet Kohli"
] |
[
"Machine Translation",
"Program Synthesis",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-11T00:00:00 |
https://openreview.net/forum?id=H1Xw62kRZ
|
https://openreview.net/pdf?id=H1Xw62kRZ
|
leveraging-grammar-and-reinforcement-learning-1
| null |
[] |
https://paperswithcode.com/paper/memory-efficient-deep-salient-object
|
1712.09558
| null | null |
Memory-Efficient Deep Salient Object Segmentation Networks on Gridized Superpixels
|
Computer vision algorithms with pixel-wise labeling tasks, such as semantic
segmentation and salient object detection, have gone through a significant
accuracy increase with the incorporation of deep learning. Deep segmentation
methods slightly modify and fine-tune pre-trained networks that have hundreds
of millions of parameters. In this work, we question the need to have such
memory demanding networks for the specific task of salient object segmentation.
To this end, we propose a way to learn a memory-efficient network from scratch
by training it only on salient object detection datasets. Our method encodes
images to gridized superpixels that preserve both the object boundaries and the
connectivity rules of regular pixels. This representation allows us to use
convolutional neural networks that operate on regular grids. By using these
encoded images, we train a memory-efficient network using only 0.048\% of the
number of parameters that other deep salient object detection networks have.
Our method shows comparable accuracy with the state-of-the-art deep salient
object detection methods and provides a faster and a much more memory-efficient
alternative to them. Due to its easy deployment, such a network is preferable
for applications in memory limited devices such as mobile phones and IoT
devices.
| null |
http://arxiv.org/abs/1712.09558v2
|
http://arxiv.org/pdf/1712.09558v2.pdf
| null |
[
"Caglar Aytekin",
"Xingyang Ni",
"Francesco Cricri",
"Lixin Fan",
"Emre Aksu"
] |
[
"Object",
"object-detection",
"Object Detection",
"RGB Salient Object Detection",
"Salient Object Detection",
"Segmentation",
"Semantic Segmentation",
"Superpixels"
] | 2017-12-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-the-importance-of-single-directions-for
|
1803.06959
| null |
r1iuQjxCZ
|
On the importance of single directions for generalization
|
Despite their ability to memorize large datasets, deep neural networks often
achieve good generalization performance. However, the differences between the
learned solutions of networks which generalize and those which do not remain
unclear. Additionally, the tuning properties of single directions (defined as
the activation of a single unit or some linear combination of units in response
to some input) have been highlighted, but their importance has not been
evaluated. Here, we connect these lines of inquiry to demonstrate that a
network's reliance on single directions is a good predictor of its
generalization performance, across networks trained on datasets with different
fractions of corrupted labels, across ensembles of networks trained on datasets
with unmodified labels, across different hyperparameters, and over the course
of training. While dropout only regularizes this quantity up to a point, batch
normalization implicitly discourages single direction reliance, in part by
decreasing the class selectivity of individual units. Finally, we find that
class selectivity is a poor predictor of task importance, suggesting not only
that networks which generalize well minimize their dependence on individual
units by reducing their selectivity, but also that individually selective units
may not be necessary for strong network performance.
|
Finally, we find that class selectivity is a poor predictor of task importance, suggesting not only that networks which generalize well minimize their dependence on individual units by reducing their selectivity, but also that individually selective units may not be necessary for strong network performance.
|
http://arxiv.org/abs/1803.06959v4
|
http://arxiv.org/pdf/1803.06959v4.pdf
|
ICLR 2018 1
|
[
"Ari S. Morcos",
"David G. T. Barrett",
"Neil C. Rabinowitz",
"Matthew Botvinick"
] |
[] | 2018-03-19T00:00:00 |
https://openreview.net/forum?id=r1iuQjxCZ
|
https://openreview.net/pdf?id=r1iuQjxCZ
|
on-the-importance-of-single-directions-for-1
| 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"
}
] |
https://paperswithcode.com/paper/gradient-energy-matching-for-distributed
|
1805.08469
| null | null |
Gradient Energy Matching for Distributed Asynchronous Gradient Descent
|
Distributed asynchronous SGD has become widely used for deep learning in
large-scale systems, but remains notorious for its instability when increasing
the number of workers. In this work, we study the dynamics of distributed
asynchronous SGD under the lens of Lagrangian mechanics. Using this
description, we introduce the concept of energy to describe the optimization
process and derive a sufficient condition ensuring its stability as long as the
collective energy induced by the active workers remains below the energy of a
target synchronous process. Making use of this criterion, we derive a stable
distributed asynchronous optimization procedure, GEM, that estimates and
maintains the energy of the asynchronous system below or equal to the energy of
sequential SGD with momentum. Experimental results highlight the stability and
speedup of GEM compared to existing schemes, even when scaling to one hundred
asynchronous workers. Results also indicate better generalization compared to
the targeted SGD with momentum.
|
Distributed asynchronous SGD has become widely used for deep learning in large-scale systems, but remains notorious for its instability when increasing the number of workers.
|
http://arxiv.org/abs/1805.08469v1
|
http://arxiv.org/pdf/1805.08469v1.pdf
| null |
[
"Joeri Hermans",
"Gilles Louppe"
] |
[] | 2018-05-22T00:00:00 | 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/mura-large-dataset-for-abnormality-detection
|
1712.06957
| null | null |
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs
|
We introduce MURA, a large dataset of musculoskeletal radiographs containing
40,561 images from 14,863 studies, where each study is manually labeled by
radiologists as either normal or abnormal. To evaluate models robustly and to
get an estimate of radiologist performance, we collect additional labels from
six board-certified Stanford radiologists on the test set, consisting of 207
musculoskeletal studies. On this test set, the majority vote of a group of
three radiologists serves as gold standard. We train a 169-layer DenseNet
baseline model to detect and localize abnormalities. Our model achieves an
AUROC of 0.929, with an operating point of 0.815 sensitivity and 0.887
specificity. We compare our model and radiologists on the Cohen's kappa
statistic, which expresses the agreement of our model and of each radiologist
with the gold standard. Model performance is comparable to the best radiologist
performance in detecting abnormalities on finger and wrist studies. However,
model performance is lower than best radiologist performance in detecting
abnormalities on elbow, forearm, hand, humerus, and shoulder studies. We
believe that the task is a good challenge for future research. To encourage
advances, we have made our dataset freely available at
https://stanfordmlgroup.github.io/competitions/mura .
|
To evaluate models robustly and to get an estimate of radiologist performance, we collect additional labels from six board-certified Stanford radiologists on the test set, consisting of 207 musculoskeletal studies.
|
http://arxiv.org/abs/1712.06957v4
|
http://arxiv.org/pdf/1712.06957v4.pdf
| null |
[
"Pranav Rajpurkar",
"Jeremy Irvin",
"Aarti Bagul",
"Daisy Ding",
"Tony Duan",
"Hershel Mehta",
"Brandon Yang",
"Kaylie Zhu",
"Dillon Laird",
"Robyn L. Ball",
"Curtis Langlotz",
"Katie Shpanskaya",
"Matthew P. Lungren",
"Andrew Y. Ng"
] |
[
"Anomaly Detection",
"Specificity"
] | 2017-12-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/rank-minimization-on-tensor-ring-a-new
|
1805.08468
| null | null |
Rank Minimization on Tensor Ring: A New Paradigm in Scalable Tensor Decomposition and Completion
|
In low-rank tensor completion tasks, due to the underlying multiple
large-scale singular value decomposition (SVD) operations and rank selection
problem of the traditional methods, they suffer from high computational cost
and high sensitivity of model complexity. In this paper, taking advantages of
high compressibility of the recently proposed tensor ring (TR) decomposition,
we propose a new model for tensor completion problem. This is achieved through
introducing convex surrogates of tensor low-rank assumption on latent tensor
ring factors, which makes it possible for the Schatten norm regularization
based models to be solved at much smaller scale. We propose two algorithms
which apply different structured Schatten norms on tensor ring factors
respectively. By the alternating direction method of multipliers (ADMM) scheme,
the tensor ring factors and the predicted tensor can be optimized
simultaneously. The experiments on synthetic data and real-world data show the
high performance and efficiency of the proposed approach.
| null |
http://arxiv.org/abs/1805.08468v1
|
http://arxiv.org/pdf/1805.08468v1.pdf
| null |
[
"Longhao Yuan",
"Chao Li",
"Danilo Mandic",
"Jianting Cao",
"Qibin Zhao"
] |
[
"Tensor Decomposition"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/shade-information-based-regularization-for-1
|
1804.10988
| null | null |
SHADE: Information Based Regularization for Deep Learning
|
Regularization is a big issue for training deep neural networks. In this
paper, we propose a new information-theory-based regularization scheme named
SHADE for SHAnnon DEcay. The originality of the approach is to define a prior
based on conditional entropy, which explicitly decouples the learning of
invariant representations in the regularizer and the learning of correlations
between inputs and labels in the data fitting term. Our second contribution is
to derive a stochastic version of the regularizer compatible with deep
learning, resulting in a tractable training scheme. We empirically validate the
efficiency of our approach to improve classification performances compared to
common regularization schemes on several standard architectures.
| null |
http://arxiv.org/abs/1804.10988v4
|
http://arxiv.org/pdf/1804.10988v4.pdf
| null |
[
"Michael Blot",
"Thomas Robert",
"Nicolas Thome",
"Matthieu Cord"
] |
[
"Deep Learning",
"General Classification"
] | 2018-04-29T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/exact-recovery-of-low-rank-tensor
|
1805.08465
| null | null |
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition under Reshuffling
|
Exact recovery of tensor decomposition (TD) methods is a desirable property in both unsupervised learning and scientific data analysis. The numerical defects of TD methods, however, limit their practical applications on real-world data. As an alternative, convex tensor decomposition (CTD) was proposed to alleviate these problems, but its exact-recovery property is not properly addressed so far. To this end, we focus on latent convex tensor decomposition (LCTD), a practically widely-used CTD model, and rigorously prove a sufficient condition for its exact-recovery property. Furthermore, we show that such property can be also achieved by a more general model than LCTD. In the new model, we generalize the classic tensor (un-)folding into reshuffling operation, a more flexible mapping to relocate the entries of the matrix into a tensor. Armed with the reshuffling operations and exact-recovery property, we explore a totally novel application for (generalized) LCTD, i.e., image steganography. Experimental results on synthetic data validate our theory, and results on image steganography show that our method outperforms the state-of-the-art methods.
| null |
https://arxiv.org/abs/1805.08465v3
|
https://arxiv.org/pdf/1805.08465v3.pdf
| null |
[
"Chao Li",
"Mohammad Emtiyaz Khan",
"Zhun Sun",
"Gang Niu",
"Bo Han",
"Shengli Xie",
"Qibin Zhao"
] |
[
"Image Steganography",
"Tensor Decomposition"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/design-and-analysis-of-diversity-based-parent
|
1805.01221
| null | null |
Design and Analysis of Diversity-Based Parent Selection Schemes for Speeding Up Evolutionary Multi-objective Optimisation
|
Parent selection in evolutionary algorithms for multi-objective optimisation
is usually performed by dominance mechanisms or indicator functions that prefer
non-dominated points. We propose to refine the parent selection on evolutionary
multi-objective optimisation with diversity-based metrics. The aim is to focus
on individuals with a high diversity contribution located in poorly explored
areas of the search space, so the chances of creating new non-dominated
individuals are better than in highly populated areas. We show by means of
rigorous runtime analysis that the use of diversity-based parent selection
mechanisms in the Simple Evolutionary Multi-objective Optimiser (SEMO) and
Global SEMO for the well known bi-objective functions ${\rm O{\small
NE}M{\small IN}M{\small AX}}$ and ${\rm LOTZ}$ can significantly improve their
performance. Our theoretical results are accompanied by experimental studies
that show a correspondence between theory and empirical results and motivate
further theoretical investigations in terms of stagnation. We show that
stagnation might occur when favouring individuals with a high diversity
contribution in the parent selection step and provide a discussion on which
scheme to use for more complex problems based on our theoretical and
experimental results.
| null |
http://arxiv.org/abs/1805.01221v2
|
http://arxiv.org/pdf/1805.01221v2.pdf
| null |
[
"Edgar Covantes Osuna",
"Wanru Gao",
"Frank Neumann",
"Dirk Sudholt"
] |
[
"Diversity",
"Evolutionary Algorithms"
] | 2018-05-03T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/structure-preserving-guided-retinal-image
|
1805.06625
| null | null |
Structure-preserving Guided Retinal Image Filtering and Its Application for Optic Disc Analysis
|
Retinal fundus photographs have been used in the diagnosis of many ocular
diseases such as glaucoma, pathological myopia, age-related macular
degeneration and diabetic retinopathy. With the development of computer
science, computer aided diagnosis has been developed to process and analyse the
retinal images automatically. One of the challenges in the analysis is that the
quality of the retinal image is often degraded. For example, a cataract in
human lens will attenuate the retinal image, just as a cloudy camera lens which
reduces the quality of a photograph. It often obscures the details in the
retinal images and posts challenges in retinal image processing and analysing
tasks. In this paper, we approximate the degradation of the retinal images as a
combination of human-lens attenuation and scattering. A novel
structure-preserving guided retinal image filtering (SGRIF) is then proposed to
restore images based on the attenuation and scattering model. The proposed
SGRIF consists of a step of global structure transferring and a step of global
edge-preserving smoothing. Our results show that the proposed SGRIF method is
able to improve the contrast of retinal images, measured by histogram flatness
measure, histogram spread and variability of local luminosity. In addition, we
further explored the benefits of SGRIF for subsequent retinal image processing
and analysing tasks. In the two applications of deep learning based optic cup
segmentation and sparse learning based cup-to-disc ratio (CDR) computation, our
results show that we are able to achieve more accurate optic cup segmentation
and CDR measurements from images processed by SGRIF.
| null |
http://arxiv.org/abs/1805.06625v2
|
http://arxiv.org/pdf/1805.06625v2.pdf
| null |
[
"Jun Cheng",
"Zhengguo Li",
"Zaiwang Gu",
"Huazhu Fu",
"Damon Wing Kee Wong",
"Jiang Liu"
] |
[
"Optic Cup Segmentation",
"Sparse Learning"
] | 2018-05-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/variational-learning-on-aggregate-outputs
|
1805.08463
| null | null |
Variational Learning on Aggregate Outputs with Gaussian Processes
|
While a typical supervised learning framework assumes that the inputs and the
outputs are measured at the same levels of granularity, many applications,
including global mapping of disease, only have access to outputs at a much
coarser level than that of the inputs. Aggregation of outputs makes
generalization to new inputs much more difficult. We consider an approach to
this problem based on variational learning with a model of output aggregation
and Gaussian processes, where aggregation leads to intractability of the
standard evidence lower bounds. We propose new bounds and tractable
approximations, leading to improved prediction accuracy and scalability to
large datasets, while explicitly taking uncertainty into account. We develop a
framework which extends to several types of likelihoods, including the Poisson
model for aggregated count data. We apply our framework to a challenging and
important problem, the fine-scale spatial modelling of malaria incidence, with
over 1 million observations.
|
While a typical supervised learning framework assumes that the inputs and the outputs are measured at the same levels of granularity, many applications, including global mapping of disease, only have access to outputs at a much coarser level than that of the inputs.
|
http://arxiv.org/abs/1805.08463v1
|
http://arxiv.org/pdf/1805.08463v1.pdf
|
NeurIPS 2018 12
|
[
"Ho Chung Leon Law",
"Dino Sejdinovic",
"Ewan Cameron",
"Tim CD Lucas",
"Seth Flaxman",
"Katherine Battle",
"Kenji Fukumizu"
] |
[
"Gaussian Processes"
] | 2018-05-22T00:00:00 |
http://papers.nips.cc/paper/7847-variational-learning-on-aggregate-outputs-with-gaussian-processes
|
http://papers.nips.cc/paper/7847-variational-learning-on-aggregate-outputs-with-gaussian-processes.pdf
|
variational-learning-on-aggregate-outputs-1
| null |
[] |
https://paperswithcode.com/paper/meta-learning-with-hessian-free-approach-in
|
1805.08462
| null | null |
Meta-Learning with Hessian-Free Approach in Deep Neural Nets Training
|
Meta-learning is a promising method to achieve efficient training method
towards deep neural net and has been attracting increases interests in recent
years. But most of the current methods are still not capable to train complex
neuron net model with long-time training process. In this paper, a novel
second-order meta-optimizer, named Meta-learning with Hessian-Free(MLHF)
approach, is proposed based on the Hessian-Free approach. Two recurrent neural
networks are established to generate the damping and the precondition matrix of
this Hessian-Free framework. A series of techniques to meta-train the MLHF
towards stable and reinforce the meta-training of this optimizer, including the
gradient calculation of $H$. Numerical experiments on deep convolution neural
nets, including CUDA-convnet and ResNet18(v2), with datasets of CIFAR10 and
ILSVRC2012, indicate that the MLHF shows good and continuous training
performance during the whole long-time training process, i.e., both the
rapid-decreasing early stage and the steadily-deceasing later stage, and so is
a promising meta-learning framework towards elevating the training efficiency
in real-world deep neural nets.
|
Meta-learning is a promising method to achieve efficient training method towards deep neural net and has been attracting increases interests in recent years.
|
http://arxiv.org/abs/1805.08462v2
|
http://arxiv.org/pdf/1805.08462v2.pdf
| null |
[
"Boyu Chen",
"Wenlian Lu",
"Ernest Fokoue"
] |
[
"Meta-Learning"
] | 2018-05-22T00:00:00 | 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
}
] |
https://paperswithcode.com/paper/high-dimension-tensor-completion-via-gradient
|
1804.01983
| null | null |
High-dimension Tensor Completion via Gradient-based Optimization Under Tensor-train Format
|
Tensor train (TT) decomposition has drawn people's attention due to its
powerful representation ability and performance stability in high-order
tensors. In this paper, we propose a novel approach to recover the missing
entries of incomplete data represented by higher-order tensors. We attempt to
find the low-rank TT decomposition of the incomplete data which captures the
latent features of the whole data and then reconstruct the missing entries. By
applying gradient descent algorithms, tensor completion problem is efficiently
solved by optimization models. We propose two TT-based algorithms: Tensor Train
Weighted Optimization (TT-WOPT) and Tensor Train Stochastic Gradient Descent
(TT-SGD) to optimize TT decomposition factors. In addition, a method named
Visual Data Tensorization (VDT) is proposed to transform visual data into
higher-order tensors, resulting in the performance improvement of our
algorithms. The experiments in synthetic data and visual data show high
efficiency and performance of our algorithms compared to the state-of-the-art
completion algorithms, especially in high-order, high missing rate, and
large-scale tensor completion situations.
|
We propose two TT-based algorithms: Tensor Train Weighted Optimization (TT-WOPT) and Tensor Train Stochastic Gradient Descent (TT-SGD) to optimize TT decomposition factors.
|
http://arxiv.org/abs/1804.01983v3
|
http://arxiv.org/pdf/1804.01983v3.pdf
| null |
[
"Longhao Yuan",
"Qibin Zhao",
"Lihua Gui",
"Jianting Cao"
] |
[
"Vocal Bursts Intensity Prediction"
] | 2018-04-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/sim-to-real-robot-learning-from-pixels-with
|
1610.04286
| null | null |
Sim-to-Real Robot Learning from Pixels with Progressive Nets
|
Applying end-to-end learning to solve complex, interactive, pixel-driven
control tasks on a robot is an unsolved problem. Deep Reinforcement Learning
algorithms are too slow to achieve performance on a real robot, but their
potential has been demonstrated in simulated environments. We propose using
progressive networks to bridge the reality gap and transfer learned policies
from simulation to the real world. The progressive net approach is a general
framework that enables reuse of everything from low-level visual features to
high-level policies for transfer to new tasks, enabling a compositional, yet
simple, approach to building complex skills. We present an early demonstration
of this approach with a number of experiments in the domain of robot
manipulation that focus on bridging the reality gap. Unlike other proposed
approaches, our real-world experiments demonstrate successful task learning
from raw visual input on a fully actuated robot manipulator. Moreover, rather
than relying on model-based trajectory optimisation, the task learning is
accomplished using only deep reinforcement learning and sparse rewards.
| null |
http://arxiv.org/abs/1610.04286v2
|
http://arxiv.org/pdf/1610.04286v2.pdf
| null |
[
"Andrei A. Rusu",
"Mel Vecerik",
"Thomas Rothörl",
"Nicolas Heess",
"Razvan Pascanu",
"Raia Hadsell"
] |
[
"Deep Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"Robot Manipulation"
] | 2016-10-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-learning-for-determining-a-near-optimal
|
1801.05463
| null | null |
Deep learning for determining a near-optimal topological design without any iteration
|
In this study, we propose a novel deep learning-based method to predict an
optimized structure for a given boundary condition and optimization setting
without using any iterative scheme. For this purpose, first, using open-source
topology optimization code, datasets of the optimized structures paired with
the corresponding information on boundary conditions and optimization settings
are generated at low (32 x 32) and high (128 x 128) resolutions. To construct
the artificial neural network for the proposed method, a convolutional neural
network (CNN)-based encoder and decoder network is trained using the training
dataset generated at low resolution. Then, as a two-stage refinement, the
conditional generative adversarial network (cGAN) is trained with the optimized
structures paired at both low and high resolutions, and is connected to the
trained CNN-based encoder and decoder network. The performance evaluation
results of the integrated network demonstrate that the proposed method can
determine a near-optimal structure in terms of pixel values and compliance with
negligible computational time.
| null |
http://arxiv.org/abs/1801.05463v3
|
http://arxiv.org/pdf/1801.05463v3.pdf
| null |
[
"Yonggyun Yu",
"Taeil Hur",
"Jaeho Jung",
"In Gwun Jang"
] |
[
"Decoder",
"Generative Adversarial Network"
] | 2018-01-13T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/conditional-network-embeddings
|
1805.07544
| null |
ryepUj0qtX
|
Conditional Network Embeddings
|
Network Embeddings (NEs) map the nodes of a given network into
$d$-dimensional Euclidean space $\mathbb{R}^d$. Ideally, this mapping is such
that `similar' nodes are mapped onto nearby points, such that the NE can be
used for purposes such as link prediction (if `similar' means being `more
likely to be connected') or classification (if `similar' means `being more
likely to have the same label'). In recent years various methods for NE have
been introduced, all following a similar strategy: defining a notion of
similarity between nodes (typically some distance measure within the network),
a distance measure in the embedding space, and a loss function that penalizes
large distances for similar nodes and small distances for dissimilar nodes.
A difficulty faced by existing methods is that certain networks are
fundamentally hard to embed due to their structural properties: (approximate)
multipartiteness, certain degree distributions, assortativity, etc. To overcome
this, we introduce a conceptual innovation to the NE literature and propose to
create \emph{Conditional Network Embeddings} (CNEs); embeddings that maximally
add information with respect to given structural properties (e.g. node degrees,
block densities, etc.). We use a simple Bayesian approach to achieve this, and
propose a block stochastic gradient descent algorithm for fitting it
efficiently. We demonstrate that CNEs are superior for link prediction and
multi-label classification when compared to state-of-the-art methods, and this
without adding significant mathematical or computational complexity. Finally,
we illustrate the potential of CNE for network visualization.
| null |
http://arxiv.org/abs/1805.07544v3
|
http://arxiv.org/pdf/1805.07544v3.pdf
|
ICLR 2019 5
|
[
"Bo Kang",
"Jefrey Lijffijt",
"Tijl De Bie"
] |
[
"General Classification",
"Link Prediction",
"Multi-Label Classification",
"MUlTI-LABEL-ClASSIFICATION"
] | 2018-05-19T00:00:00 |
https://openreview.net/forum?id=ryepUj0qtX
|
https://openreview.net/pdf?id=ryepUj0qtX
|
conditional-network-embeddings-1
| null |
[] |
https://paperswithcode.com/paper/qbf-as-an-alternative-to-courcelles-theorem
|
1805.08456
| null | null |
QBF as an Alternative to Courcelle's Theorem
|
We propose reductions to quantified Boolean formulas (QBF) as a new approach
to showing fixed-parameter linear algorithms for problems parameterized by
treewidth. We demonstrate the feasibility of this approach by giving new
algorithms for several well-known problems from artificial intelligence that
are in general complete for the second level of the polynomial hierarchy. By
reduction from QBF we show that all resulting algorithms are essentially
optimal in their dependence on the treewidth. Most of the problems that we
consider were already known to be fixed-parameter linear by using Courcelle's
Theorem or dynamic programming, but we argue that our approach has clear
advantages over these techniques: on the one hand, in contrast to Courcelle's
Theorem, we get concrete and tight guarantees for the runtime dependence on the
treewidth. On the other hand, we avoid tedious dynamic programming and, after
showing some normalization results for CNF-formulas, our upper bounds often
boil down to a few lines.
| null |
http://arxiv.org/abs/1805.08456v1
|
http://arxiv.org/pdf/1805.08456v1.pdf
| null |
[
"Michael Lampis",
"Stefan Mengel",
"Valia Mitsou"
] |
[] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/context-aware-sequence-to-sequence-models-for
|
1805.08455
| null | null |
Context-Aware Sequence-to-Sequence Models for Conversational Systems
|
This work proposes a novel approach based on sequence-to-sequence (seq2seq)
models for context-aware conversational systems. Exist- ing seq2seq models have
been shown to be good for generating natural responses in a data-driven
conversational system. However, they still lack mechanisms to incorporate
previous conversation turns. We investigate RNN-based methods that efficiently
integrate previous turns as a context for generating responses. Overall, our
experimental results based on human judgment demonstrate the feasibility and
effectiveness of the proposed approach.
| null |
http://arxiv.org/abs/1805.08455v1
|
http://arxiv.org/pdf/1805.08455v1.pdf
| null |
[
"Silje Christensen",
"Simen Johnsrud",
"Massimiliano Ruocco",
"Heri Ramampiaro"
] |
[] | 2018-05-22T00:00:00 | null | null | null | 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/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": null,
"description": "An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.\r\n\r\n(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))\r\n\r\n(Introduced by Hochreiter and Schmidhuber)",
"full_name": "Long Short-Term Memory",
"introduced_year": 1997,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Recurrent Neural Networks",
"parent": null
},
"name": "LSTM",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "**Seq2Seq**, or **Sequence To Sequence**, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one [LSTM](https://paperswithcode.com/method/lstm), the *encoder*, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a context vector), and then to use another LSTM, the *decoder*, to extract the output sequence\r\nfrom that vector. The second LSTM is essentially a recurrent neural network language model except that it is conditioned on the input sequence.\r\n\r\n(Note that this page refers to the original seq2seq not general sequence-to-sequence models)",
"full_name": "Sequence to Sequence",
"introduced_year": 2000,
"main_collection": {
"area": "Sequential",
"description": "",
"name": "Sequence To Sequence Models",
"parent": null
},
"name": "Seq2Seq",
"source_title": "Sequence to Sequence Learning with Neural Networks",
"source_url": "http://arxiv.org/abs/1409.3215v3"
}
] |
https://paperswithcode.com/paper/avatar-net-multi-scale-zero-shot-style
|
1805.03857
| null | null |
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
|
Zero-shot artistic style transfer is an important image synthesis problem
aiming at transferring arbitrary style into content images. However, the
trade-off between the generalization and efficiency in existing methods impedes
a high quality zero-shot style transfer in real-time. In this paper, we resolve
this dilemma and propose an efficient yet effective Avatar-Net that enables
visually plausible multi-scale transfer for arbitrary style. The key ingredient
of our method is a style decorator that makes up the content features by
semantically aligned style features from an arbitrary style image, which does
not only holistically match their feature distributions but also preserve
detailed style patterns in the decorated features. By embedding this module
into an image reconstruction network that fuses multi-scale style abstractions,
the Avatar-Net renders multi-scale stylization for any style image in one
feed-forward pass. We demonstrate the state-of-the-art effectiveness and
efficiency of the proposed method in generating high-quality stylized images,
with a series of applications include multiple style integration, video
stylization and etc.
|
Zero-shot artistic style transfer is an important image synthesis problem aiming at transferring arbitrary style into content images.
|
http://arxiv.org/abs/1805.03857v2
|
http://arxiv.org/pdf/1805.03857v2.pdf
|
CVPR 2018 6
|
[
"Lu Sheng",
"Ziyi Lin",
"Jing Shao",
"Xiaogang Wang"
] |
[
"Image Generation",
"Image Reconstruction",
"Style Transfer"
] | 2018-05-10T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Sheng_Avatar-Net_Multi-Scale_Zero-Shot_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Sheng_Avatar-Net_Multi-Scale_Zero-Shot_CVPR_2018_paper.pdf
|
avatar-net-multi-scale-zero-shot-style-1
| null |
[] |
https://paperswithcode.com/paper/classification-uncertainty-of-deep-neural
|
1805.08440
| null | null |
Classification Uncertainty of Deep Neural Networks Based on Gradient Information
|
We study the quantification of uncertainty of Convolutional Neural Networks
(CNNs) based on gradient metrics. Unlike the classical softmax entropy, such
metrics gather information from all layers of the CNN. We show for the EMNIST
digits data set that for several such metrics we achieve the same meta
classification accuracy -- i.e. the task of classifying predictions as correct
or incorrect without knowing the actual label -- as for entropy thresholding.
We apply meta classification to unknown concepts (out-of-distribution samples)
-- EMNIST/Omniglot letters, CIFAR10 and noise -- and demonstrate that meta
classification rates for unknown concepts can be increased when using entropy
together with several gradient based metrics as input quantities for a meta
classifier. Meta classifiers only trained on the uncertainty metrics of known
concepts, i.e. EMNIST digits, usually do not perform equally well for all
unknown concepts. If we however allow the meta classifier to be trained on
uncertainty metrics for some out-of-distribution samples, meta classification
for concepts remote from EMNIST digits (then termed known unknowns) can be
improved considerably.
| null |
http://arxiv.org/abs/1805.08440v2
|
http://arxiv.org/pdf/1805.08440v2.pdf
| null |
[
"Philipp Oberdiek",
"Matthias Rottmann",
"Hanno Gottschalk"
] |
[
"Classification",
"General Classification",
"Known Unknowns"
] | 2018-05-22T00:00:00 | null | null | null | 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/paracompositionality-mwes-and-argument
|
1805.08438
| null | null |
Paracompositionality, MWEs and Argument Substitution
|
Multi-word expressions, verb-particle constructions, idiomatically combining
phrases, and phrasal idioms have something in common: not all of their elements
contribute to the argument structure of the predicate implicated by the
expression.
Radically lexicalized theories of grammar that avoid string-, term-, logical
form-, and tree-writing, and categorial grammars that avoid wrap operation,
make predictions about the categories involved in verb-particles and phrasal
idioms. They may require singleton types, which can only substitute for one
value, not just for one kind of value. These types are asymmetric: they can be
arguments only. They also narrowly constrain the kind of semantic value that
can correspond to such syntactic categories. Idiomatically combining phrases do
not subcategorize for singleton types, and they exploit another locally
computable and compositional property of a correspondence, that every syntactic
expression can project its head word. Such MWEs can be seen as empirically
realized categorial possibilities, rather than lacuna in a theory of
lexicalizable syntactic categories.
| null |
http://arxiv.org/abs/1805.08438v1
|
http://arxiv.org/pdf/1805.08438v1.pdf
| null |
[
"Cem Bozsahin",
"Arzu Burcu Guven"
] |
[] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/rpc-considered-harmful-fast-distributed-deep
|
1805.08430
| null | null |
RPC Considered Harmful: Fast Distributed Deep Learning on RDMA
|
Deep learning emerges as an important new resource-intensive workload and has
been successfully applied in computer vision, speech, natural language
processing, and so on. Distributed deep learning is becoming a necessity to
cope with growing data and model sizes. Its computation is typically
characterized by a simple tensor data abstraction to model multi-dimensional
matrices, a data-flow graph to model computation, and iterative executions with
relatively frequent synchronizations, thereby making it substantially different
from Map/Reduce style distributed big data computation.
RPC, commonly used as the communication primitive, has been adopted by
popular deep learning frameworks such as TensorFlow, which uses gRPC. We show
that RPC is sub-optimal for distributed deep learning computation, especially
on an RDMA-capable network. The tensor abstraction and data-flow graph, coupled
with an RDMA network, offers the opportunity to reduce the unnecessary overhead
(e.g., memory copy) without sacrificing programmability and generality. In
particular, from a data access point of view, a remote machine is abstracted
just as a "device" on an RDMA channel, with a simple memory interface for
allocating, reading, and writing memory regions. Our graph analyzer looks at
both the data flow graph and the tensors to optimize memory allocation and
remote data access using this interface. The result is up to 25 times speedup
in representative deep learning benchmarks against the standard gRPC in
TensorFlow and up to 169% improvement even against an RPC implementation
optimized for RDMA, leading to faster convergence in the training process.
| null |
http://arxiv.org/abs/1805.08430v1
|
http://arxiv.org/pdf/1805.08430v1.pdf
| null |
[
"Jilong Xue",
"Youshan Miao",
"Cheng Chen",
"Ming Wu",
"Lintao Zhang",
"Lidong Zhou"
] |
[
"Deep Learning"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/bayesian-inference-of-regular-expressions
|
1805.08427
| null | null |
Bayesian Inference of Regular Expressions from Human-Generated Example Strings
|
In programming by example, users "write" programs by generating a small
number of input-output examples and asking the computer to synthesize
consistent programs. We consider a challenging problem in this domain: learning
regular expressions (regexes) from positive and negative example strings. This
problem is challenging, as (1) user-generated examples may not be informative
enough to sufficiently constrain the hypothesis space, and (2) even if
user-generated examples are in principle informative, there is still a massive
search space to examine. We frame regex induction as the problem of inferring a
probabilistic regular grammar and propose an efficient inference approach that
uses a novel stochastic process recognition model. This model incrementally
"grows" a grammar using positive examples as a scaffold. We show that this
approach is competitive with human ability to learn regexes from examples.
| null |
http://arxiv.org/abs/1805.08427v2
|
http://arxiv.org/pdf/1805.08427v2.pdf
| null |
[
"Long Ouyang"
] |
[
"Bayesian Inference"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/cubes3d-neural-network-based-optical-flow-in
|
1804.09004
| null | null |
Cubes3D: Neural Network based Optical Flow in Omnidirectional Image Scenes
|
Optical flow estimation with convolutional neural networks (CNNs) has
recently solved various tasks of computer vision successfully. In this paper we
adapt a state-of-the-art approach for optical flow estimation to
omnidirectional images. We investigate CNN architectures to determine high
motion variations caused by the geometry of fish-eye images. Further we
determine the qualitative influence of texture on the non-rigid object to the
motion vectors. For evaluation of the results we create ground truth motion
fields synthetically. The ground truth contains cubes with static background.
We test variations of pre-trained FlowNet 2.0 architectures by indicating
common error metrics. We generate competitive results for the motion of the
foreground with inhomogeneous texture on the moving object.
|
We investigate CNN architectures to determine high motion variations caused by the geometry of fish-eye images.
|
http://arxiv.org/abs/1804.09004v2
|
http://arxiv.org/pdf/1804.09004v2.pdf
| null |
[
"André Apitzsch",
"Roman Seidel",
"Gangolf Hirtz"
] |
[
"Object",
"Optical Flow Estimation"
] | 2018-04-24T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/planar-object-tracking-in-the-wild-a
|
1703.07938
| null | null |
Planar Object Tracking in the Wild: A Benchmark
|
Planar object tracking is an actively studied problem in vision-based robotic
applications. While several benchmarks have been constructed for evaluating
state-of-the-art algorithms, there is a lack of video sequences captured in the
wild rather than in constrained laboratory environment. In this paper, we
present a carefully designed planar object tracking benchmark containing 210
videos of 30 planar objects sampled in the natural environment. In particular,
for each object, we shoot seven videos involving various challenging factors,
namely scale change, rotation, perspective distortion, motion blur, occlusion,
out-of-view, and unconstrained. The ground truth is carefully annotated
semi-manually to ensure the quality. Moreover, eleven state-of-the-art
algorithms are evaluated on the benchmark using two evaluation metrics, with
detailed analysis provided for the evaluation results. We expect the proposed
benchmark to benefit future studies on planar object tracking.
| null |
http://arxiv.org/abs/1703.07938v2
|
http://arxiv.org/pdf/1703.07938v2.pdf
| null |
[
"Pengpeng Liang",
"Yifan Wu",
"Hu Lu",
"Liming Wang",
"Chunyuan Liao",
"Haibin Ling"
] |
[
"Homography Estimation",
"Object",
"Object Tracking"
] | 2017-03-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-topic-models-by-neighborhood
|
1802.08012
| null | null |
Learning Topic Models by Neighborhood Aggregation
|
Topic models are frequently used in machine learning owing to their high interpretability and modular structure. However, extending a topic model to include a supervisory signal, to incorporate pre-trained word embedding vectors and to include a nonlinear output function is not an easy task because one has to resort to a highly intricate approximate inference procedure. The present paper shows that topic modeling with pre-trained word embedding vectors can be viewed as implementing a neighborhood aggregation algorithm where messages are passed through a network defined over words. From the network view of topic models, nodes correspond to words in a document and edges correspond to either a relationship describing co-occurring words in a document or a relationship describing the same word in the corpus. The network view allows us to extend the model to include supervisory signals, incorporate pre-trained word embedding vectors and include a nonlinear output function in a simple manner. In experiments, we show that our approach outperforms the state-of-the-art supervised Latent Dirichlet Allocation implementation in terms of held-out document classification tasks.
| null |
https://arxiv.org/abs/1802.08012v6
|
https://arxiv.org/pdf/1802.08012v6.pdf
| null |
[
"Ryohei Hisano"
] |
[
"Document Classification",
"Topic Models"
] | 2018-02-22T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "Interpretability",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images for downstream tasks such as classification and object detection. The most popular subcategory are convolutional neural networks. Below you can find a continuously updated list of image models.",
"name": "Image Models",
"parent": null
},
"name": "Interpretability",
"source_title": "CAM: Causal additive models, high-dimensional order search and penalized regression",
"source_url": "http://arxiv.org/abs/1310.1533v2"
}
] |
https://paperswithcode.com/paper/enriched-long-term-recurrent-convolutional
|
1805.08417
| null | null |
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
|
Facial micro-expression (ME) recognition has posed a huge challenge to
researchers for its subtlety in motion and limited databases. Recently,
handcrafted techniques have achieved superior performance in micro-expression
recognition but at the cost of domain specificity and cumbersome parametric
tunings. In this paper, we propose an Enriched Long-term Recurrent
Convolutional Network (ELRCN) that first encodes each micro-expression frame
into a feature vector through CNN module(s), then predicts the micro-expression
by passing the feature vector through a Long Short-term Memory (LSTM) module.
The framework contains two different network variants: (1) Channel-wise
stacking of input data for spatial enrichment, (2) Feature-wise stacking of
features for temporal enrichment. We demonstrate that the proposed approach is
able to achieve reasonably good performance, without data augmentation. In
addition, we also present ablation studies conducted on the framework and
visualizations of what CNN "sees" when predicting the micro-expression classes.
|
Facial micro-expression (ME) recognition has posed a huge challenge to researchers for its subtlety in motion and limited databases.
|
http://arxiv.org/abs/1805.08417v1
|
http://arxiv.org/pdf/1805.08417v1.pdf
| null |
[
"Huai-Qian Khor",
"John See",
"Raphael C. -W. Phan",
"Weiyao Lin"
] |
[
"Data Augmentation",
"Micro Expression Recognition",
"Micro-Expression Recognition",
"Specificity"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/lipschitz-margin-training-scalable
|
1802.04034
| null | null |
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
|
High sensitivity of neural networks against malicious perturbations on inputs
causes security concerns. To take a steady step towards robust classifiers, we
aim to create neural network models provably defended from perturbations. Prior
certification work requires strong assumptions on network structures and
massive computational costs, and thus the range of their applications was
limited. From the relationship between the Lipschitz constants and prediction
margins, we present a computationally efficient calculation technique to
lower-bound the size of adversarial perturbations that can deceive networks,
and that is widely applicable to various complicated networks. Moreover, we
propose an efficient training procedure that robustifies networks and
significantly improves the provably guarded areas around data points. In
experimental evaluations, our method showed its ability to provide a
non-trivial guarantee and enhance robustness for even large networks.
|
High sensitivity of neural networks against malicious perturbations on inputs causes security concerns.
|
http://arxiv.org/abs/1802.04034v3
|
http://arxiv.org/pdf/1802.04034v3.pdf
|
NeurIPS 2018 12
|
[
"Yusuke Tsuzuku",
"Issei Sato",
"Masashi Sugiyama"
] |
[] | 2018-02-12T00:00:00 |
http://papers.nips.cc/paper/7889-lipschitz-margin-training-scalable-certification-of-perturbation-invariance-for-deep-neural-networks
|
http://papers.nips.cc/paper/7889-lipschitz-margin-training-scalable-certification-of-perturbation-invariance-for-deep-neural-networks.pdf
|
lipschitz-margin-training-scalable-1
| null |
[] |
https://paperswithcode.com/paper/training-convolutional-networks-with-web
|
1805.08416
| null | null |
Training Convolutional Networks with Web Images
|
In this thesis we investigate the effect of using web images to build a large
scale database to be used along a deep learning method for a classification
task. We replicate the ImageNet large scale database (ILSVRC-2012) from images
collected from the web using 4 different download strategies varying: the
search engine, the query and the image resolution. As a deep learning method,
we will choose the Convolutional Neural Network that was very successful with
recognition tasks; the AlexNet.
| null |
http://arxiv.org/abs/1805.08416v1
|
http://arxiv.org/pdf/1805.08416v1.pdf
| null |
[
"Nizar Massouh"
] |
[
"Deep Learning",
"General Classification"
] | 2018-05-22T00:00:00 | null | null | null | 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/estimating-the-rating-of-reviewers-based-on
|
1805.08415
| null | null |
Estimating the Rating of Reviewers Based on the Text
|
User-generated texts such as reviews and social media are valuable sources of
information. Online reviews are important assets for users to buy a product,
see a movie, or make a decision. Therefore, rating of a review is one of the
reliable factors for all users to read and trust the reviews. This paper
analyzes the texts of the reviews to evaluate and predict the ratings.
Moreover, we study the effect of lexical features generated from text as well
as sentimental words on the accuracy of rating prediction. Our analysis show
that words with high information gain score are more efficient compared to
words with high TF-IDF value. In addition, we explore the best number of
features for predicting the ratings of the reviews.
| null |
http://arxiv.org/abs/1805.08415v1
|
http://arxiv.org/pdf/1805.08415v1.pdf
| null |
[
"Mohammadamir Kavousi",
"Sepehr Saadatmand"
] |
[] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/wikipedia-for-smart-machines-and-double-deep
|
1711.06517
| null | null |
Wikipedia for Smart Machines and Double Deep Machine Learning
|
Very important breakthroughs in data centric deep learning algorithms led to
impressive performance in transactional point applications of Artificial
Intelligence (AI) such as Face Recognition, or EKG classification. With all due
appreciation, however, knowledge blind data only machine learning algorithms
have severe limitations for non-transactional AI applications, such as medical
diagnosis beyond the EKG results. Such applications require deeper and broader
knowledge in their problem solving capabilities, e.g. integrating anatomy and
physiology knowledge with EKG results and other patient findings. Following a
review and illustrations of such limitations for several real life AI
applications, we point at ways to overcome them. The proposed Wikipedia for
Smart Machines initiative aims at building repositories of software structures
that represent humanity science & technology knowledge in various parts of
life; knowledge that we all learn in schools, universities and during our
professional life. Target readers for these repositories are smart machines;
not human. AI software developers will have these Reusable Knowledge structures
readily available, hence, the proposed name ReKopedia. Big Data is by now a
mature technology, it is time to focus on Big Knowledge. Some will be derived
from data, some will be obtained from mankind gigantic repository of knowledge.
Wikipedia for smart machines along with the new Double Deep Learning approach
offer a paradigm for integrating datacentric deep learning algorithms with
algorithms that leverage deep knowledge, e.g. evidential reasoning and
causality reasoning. For illustration, a project is described to produce
ReKopedia knowledge modules for medical diagnosis of about 1,000 disorders.
Data is important, but knowledge deep, basic, and commonsense is equally
important.
| null |
http://arxiv.org/abs/1711.06517v2
|
http://arxiv.org/pdf/1711.06517v2.pdf
| null |
[
"Moshe Ben-Bassat"
] |
[
"Anatomy",
"BIG-bench Machine Learning",
"Deep Learning",
"Face Recognition",
"Medical Diagnosis"
] | 2017-11-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dlbi-deep-learning-guided-bayesian-inference
|
1805.07777
| null | null |
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy
|
Super-resolution fluorescence microscopy, with a resolution beyond the
diffraction limit of light, has become an indispensable tool to directly
visualize biological structures in living cells at a nanometer-scale
resolution. Despite advances in high-density super-resolution fluorescent
techniques, existing methods still have bottlenecks, including extremely long
execution time, artificial thinning and thickening of structures, and lack of
ability to capture latent structures. Here we propose a novel deep learning
guided Bayesian inference approach, DLBI, for the time-series analysis of
high-density fluorescent images. Our method combines the strength of deep
learning and statistical inference, where deep learning captures the underlying
distribution of the fluorophores that are consistent with the observed
time-series fluorescent images by exploring local features and correlation
along time-axis, and statistical inference further refines the ultrastructure
extracted by deep learning and endues physical meaning to the final image.
Comprehensive experimental results on both real and simulated datasets
demonstrate that our method provides more accurate and realistic local patch
and large-field reconstruction than the state-of-the-art method, the 3B
analysis, while our method is more than two orders of magnitude faster. The
main program is available at https://github.com/lykaust15/DLBI
|
Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image.
|
http://arxiv.org/abs/1805.07777v3
|
http://arxiv.org/pdf/1805.07777v3.pdf
| null |
[
"Yu Li",
"Fan Xu",
"Fa Zhang",
"Pingyong Xu",
"Mingshu Zhang",
"Ming Fan",
"Lihua Li",
"Xin Gao",
"Renmin Han"
] |
[
"Bayesian Inference",
"Deep Learning",
"Super-Resolution",
"Time Series",
"Time Series Analysis"
] | 2018-05-20T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/spherical-convolutional-neural-network-for-3d
|
1805.07872
| null | null |
Spherical Convolutional Neural Network for 3D Point Clouds
|
We propose a neural network for 3D point cloud processing that exploits
`spherical' convolution kernels and octree partitioning of space. The proposed
metric-based spherical kernels systematically quantize point neighborhoods to
identify local geometric structures in data, while maintaining the properties
of translation-invariance and asymmetry. The network architecture itself is
guided by octree data structuring that takes full advantage of the sparse
nature of irregular point clouds. We specify spherical kernels with the help of
neurons in each layer that in turn are associated with spatial locations. We
exploit this association to avert dynamic kernel generation during network
training, that enables efficient learning with high resolution point clouds. We
demonstrate the utility of the spherical convolutional neural network for 3D
object classification on standard benchmark datasets.
| null |
http://arxiv.org/abs/1805.07872v2
|
http://arxiv.org/pdf/1805.07872v2.pdf
| null |
[
"Huan Lei",
"Naveed Akhtar",
"Ajmal Mian"
] |
[
"3D Object Classification",
"General Classification",
"Translation"
] | 2018-05-21T00:00:00 | 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
}
] |
https://paperswithcode.com/paper/learning-to-optimize-via-wasserstein-deep
|
1805.08395
| null | null |
Learning to Optimize via Wasserstein Deep Inverse Optimal Control
|
We study the inverse optimal control problem in social sciences: we aim at
learning a user's true cost function from the observed temporal behavior. In
contrast to traditional phenomenological works that aim to learn a generative
model to fit the behavioral data, we propose a novel variational principle and
treat user as a reinforcement learning algorithm, which acts by optimizing his
cost function. We first propose a unified KL framework that generalizes
existing maximum entropy inverse optimal control methods. We further propose a
two-step Wasserstein inverse optimal control framework. In the first step, we
compute the optimal measure with a novel mass transport equation. In the second
step, we formulate the learning problem as a generative adversarial network. In
two real world experiments - recommender systems and social networks, we show
that our framework obtains significant performance gains over both existing
inverse optimal control methods and point process based generative models.
| null |
http://arxiv.org/abs/1805.08395v1
|
http://arxiv.org/pdf/1805.08395v1.pdf
| null |
[
"Yichen Wang",
"Le Song",
"Hongyuan Zha"
] |
[
"Generative Adversarial Network",
"Recommendation Systems",
"Reinforcement Learning"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/on-the-convergence-and-robustness-of-training
|
1802.08249
| null | null |
On the Convergence and Robustness of Training GANs with Regularized Optimal Transport
|
Generative Adversarial Networks (GANs) are one of the most practical methods
for learning data distributions. A popular GAN formulation is based on the use
of Wasserstein distance as a metric between probability distributions.
Unfortunately, minimizing the Wasserstein distance between the data
distribution and the generative model distribution is a computationally
challenging problem as its objective is non-convex, non-smooth, and even hard
to compute. In this work, we show that obtaining gradient information of the
smoothed Wasserstein GAN formulation, which is based on regularized Optimal
Transport (OT), is computationally effortless and hence one can apply first
order optimization methods to minimize this objective. Consequently, we
establish theoretical convergence guarantee to stationarity for a proposed
class of GAN optimization algorithms. Unlike the original non-smooth
formulation, our algorithm only requires solving the discriminator to
approximate optimality. We apply our method to learning MNIST digits as well as
CIFAR-10images. Our experiments show that our method is computationally
efficient and generates images comparable to the state of the art algorithms
given the same architecture and computational power.
| null |
http://arxiv.org/abs/1802.08249v2
|
http://arxiv.org/pdf/1802.08249v2.pdf
|
NeurIPS 2018 12
|
[
"Maziar Sanjabi",
"Jimmy Ba",
"Meisam Razaviyayn",
"Jason D. Lee"
] |
[] | 2018-02-22T00:00:00 |
http://papers.nips.cc/paper/7940-on-the-convergence-and-robustness-of-training-gans-with-regularized-optimal-transport
|
http://papers.nips.cc/paper/7940-on-the-convergence-and-robustness-of-training-gans-with-regularized-optimal-transport.pdf
|
on-the-convergence-and-robustness-of-training-1
| 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/an-incremental-path-following-splitting
|
1801.10119
| null | null |
An Incremental Path-Following Splitting Method for Linearly Constrained Nonconvex Nonsmooth Programs
|
The stationary point of Problem 2 is NOT the stationary point of Problem 1.
We are sorry and we are working on fixing this error.
| null |
http://arxiv.org/abs/1801.10119v6
|
http://arxiv.org/pdf/1801.10119v6.pdf
| null |
[
"Linbo Qiao",
"Wei Liu",
"Steven Hoi"
] |
[] | 2018-01-30T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/joint-image-captioning-and-question-answering
|
1805.08389
| null | null |
Joint Image Captioning and Question Answering
|
Answering visual questions need acquire daily common knowledge and model the
semantic connection among different parts in images, which is too difficult for
VQA systems to learn from images with the only supervision from answers.
Meanwhile, image captioning systems with beam search strategy tend to generate
similar captions and fail to diversely describe images. To address the
aforementioned issues, we present a system to have these two tasks compensate
with each other, which is capable of jointly producing image captions and
answering visual questions. In particular, we utilize question and image
features to generate question-related captions and use the generated captions
as additional features to provide new knowledge to the VQA system. For image
captioning, our system attains more informative results in term of the relative
improvements on VQA tasks as well as competitive results using automated
metrics. Applying our system to the VQA tasks, our results on VQA v2 dataset
achieve 65.8% using generated captions and 69.1% using annotated captions in
validation set and 68.4% in the test-standard set. Further, an ensemble of 10
models results in 69.7% in the test-standard split.
| null |
http://arxiv.org/abs/1805.08389v1
|
http://arxiv.org/pdf/1805.08389v1.pdf
| null |
[
"Jialin Wu",
"Zeyuan Hu",
"Raymond J. Mooney"
] |
[
"Image Captioning",
"Question Answering",
"Visual Question Answering (VQA)"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/designing-the-game-to-play-optimizing-payoff
|
1805.01987
| null | null |
Designing the Game to Play: Optimizing Payoff Structure in Security Games
|
Effective game-theoretic modeling of defender-attacker behavior is becoming
increasingly important. In many domains, the defender functions not only as a
player but also the designer of the game's payoff structure. We study
Stackelberg Security Games where the defender, in addition to allocating
defensive resources to protect targets from the attacker, can strategically
manipulate the attacker's payoff under budget constraints in weighted L^p-norm
form regarding the amount of change. Focusing on problems with weighted
L^1-norm form constraint, we present (i) a mixed integer linear program-based
algorithm with approximation guarantee; (ii) a branch-and-bound based algorithm
with improved efficiency achieved by effective pruning; (iii) a polynomial time
approximation scheme for a special but practical class of problems. In
addition, we show that problems under budget constraints in L^0-norm form and
weighted L^\infty-norm form can be solved in polynomial time. We provide an
extensive experimental evaluation of our proposed algorithms.
| null |
http://arxiv.org/abs/1805.01987v2
|
http://arxiv.org/pdf/1805.01987v2.pdf
| null |
[
"Zheyuan Ryan Shi",
"Ziye Tang",
"Long Tran-Thanh",
"Rohit Singh",
"Fei Fang"
] |
[
"Form"
] | 2018-05-05T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-and-transferring-ids-representation
|
1712.08289
| null | null |
Learning and Transferring IDs Representation in E-commerce
|
Many machine intelligence techniques are developed in E-commerce and one of
the most essential components is the representation of IDs, including user ID,
item ID, product ID, store ID, brand ID, category ID etc. The classical
encoding based methods (like one-hot encoding) are inefficient in that it
suffers sparsity problems due to its high dimension, and it cannot reflect the
relationships among IDs, either homogeneous or heterogeneous ones. In this
paper, we propose an embedding based framework to learn and transfer the
representation of IDs. As the implicit feedbacks of users, a tremendous amount
of item ID sequences can be easily collected from the interactive sessions. By
jointly using these informative sequences and the structural connections among
IDs, all types of IDs can be embedded into one low-dimensional semantic space.
Subsequently, the learned representations are utilized and transferred in four
scenarios: (i) measuring the similarity between items, (ii) transferring from
seen items to unseen items, (iii) transferring across different domains, (iv)
transferring across different tasks. We deploy and evaluate the proposed
approach in Hema App and the results validate its effectiveness.
| null |
http://arxiv.org/abs/1712.08289v4
|
http://arxiv.org/pdf/1712.08289v4.pdf
| null |
[
"Kui Zhao",
"Yuechuan Li",
"Zhaoqian Shuai",
"Cheng Yang"
] |
[] | 2017-12-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/adversarial-examples-that-fool-both-computer
|
1802.08195
| null | null |
Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
|
Machine learning models are vulnerable to adversarial examples: small changes
to images can cause computer vision models to make mistakes such as identifying
a school bus as an ostrich. However, it is still an open question whether
humans are prone to similar mistakes. Here, we address this question by
leveraging recent techniques that transfer adversarial examples from computer
vision models with known parameters and architecture to other models with
unknown parameters and architecture, and by matching the initial processing of
the human visual system. We find that adversarial examples that strongly
transfer across computer vision models influence the classifications made by
time-limited human observers.
| null |
http://arxiv.org/abs/1802.08195v3
|
http://arxiv.org/pdf/1802.08195v3.pdf
|
NeurIPS 2018 12
|
[
"Gamaleldin F. Elsayed",
"Shreya Shankar",
"Brian Cheung",
"Nicolas Papernot",
"Alex Kurakin",
"Ian Goodfellow",
"Jascha Sohl-Dickstein"
] |
[
"BIG-bench Machine Learning",
"Open-Ended Question Answering"
] | 2018-02-22T00:00:00 |
http://papers.nips.cc/paper/7647-adversarial-examples-that-fool-both-computer-vision-and-time-limited-humans
|
http://papers.nips.cc/paper/7647-adversarial-examples-that-fool-both-computer-vision-and-time-limited-humans.pdf
|
adversarial-examples-that-fool-both-computer-1
| null |
[] |
https://paperswithcode.com/paper/learning-markov-clustering-networks-for-scene
|
1805.08365
| null | null |
Learning Markov Clustering Networks for Scene Text Detection
|
A novel framework named Markov Clustering Network (MCN) is proposed for fast
and robust scene text detection. MCN predicts instance-level bounding boxes by
firstly converting an image into a Stochastic Flow Graph (SFG) and then
performing Markov Clustering on this graph. Our method can detect text objects
with arbitrary size and orientation without prior knowledge of object size. The
stochastic flow graph encode objects' local correlation and semantic
information. An object is modeled as strongly connected nodes, which allows
flexible bottom-up detection for scale-varying and rotated objects. MCN
generates bounding boxes without using Non-Maximum Suppression, and it can be
fully parallelized on GPUs. The evaluation on public benchmarks shows that our
method outperforms the existing methods by a large margin in detecting
multioriented text objects. MCN achieves new state-of-art performance on
challenging MSRA-TD500 dataset with precision of 0.88, recall of 0.79 and
F-score of 0.83. Also, MCN achieves realtime inference with frame rate of 34
FPS, which is $1.5\times$ speedup when compared with the fastest scene text
detection algorithm.
| null |
http://arxiv.org/abs/1805.08365v1
|
http://arxiv.org/pdf/1805.08365v1.pdf
|
CVPR 2018 6
|
[
"Zichuan Liu",
"Guosheng Lin",
"Sheng Yang",
"Jiashi Feng",
"Weisi Lin",
"Wang Ling Goh"
] |
[
"Clustering",
"Scene Text Detection",
"Text Detection"
] | 2018-05-22T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_Learning_Markov_Clustering_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_Learning_Markov_Clustering_CVPR_2018_paper.pdf
|
learning-markov-clustering-networks-for-scene-1
| null |
[] |
https://paperswithcode.com/paper/adversarial-training-for-disease-prediction
|
1711.04126
| null | null |
Adversarial Training for Disease Prediction from Electronic Health Records with Missing Data
|
Electronic health records (EHRs) have contributed to the computerization of
patient records and can thus be used not only for efficient and systematic
medical services, but also for research on biomedical data science. However,
there are many missing values in EHRs when provided in matrix form, which is an
important issue in many biomedical EHR applications. In this paper, we propose
a two-stage framework that includes missing data imputation and disease
prediction to address the missing data problem in EHRs. We compared the disease
prediction performance of generative adversarial networks (GANs) and
conventional learning algorithms in combination with missing data prediction
methods. As a result, we obtained a level of accuracy of 0.9777, sensitivity of
0.9521, specificity of 0.9925, area under the receiver operating characteristic
curve (AUC-ROC) of 0.9889, and F-score of 0.9688 with a stacked autoencoder as
the missing data prediction method and an auxiliary classifier GAN (AC-GAN) as
the disease prediction method. The comparison results show that a combination
of a stacked autoencoder and an AC-GAN significantly outperforms other existing
approaches. Our results suggest that the proposed framework is more robust for
disease prediction from EHRs with missing data.
| null |
http://arxiv.org/abs/1711.04126v4
|
http://arxiv.org/pdf/1711.04126v4.pdf
| null |
[
"Uiwon Hwang",
"Sungwoon Choi",
"Han-Byoel Lee",
"Sungroh Yoon"
] |
[
"Disease Prediction",
"Imputation",
"Missing Values",
"Prediction",
"Specificity"
] | 2017-11-11T00:00:00 | 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"
},
{
"code_snippet_url": "",
"description": "**Auxiliary Classifiers** are type of architectural component that seek to improve the convergence of very deep networks. They are classifier heads we attach to layers before the end of the network. The motivation is to push useful gradients to the lower layers to make them immediately useful and improve the convergence during training by combatting the vanishing gradient problem. They are notably used in the Inception family of convolutional neural networks.",
"full_name": "Auxiliary Classifier",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "The following is a list of miscellaneous components used in neural networks.",
"name": "Miscellaneous Components",
"parent": null
},
"name": "Auxiliary Classifier",
"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, 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/difnet-semantic-segmentation-by-diffusion
|
1805.08015
| null | null |
DifNet: Semantic Segmentation by Diffusion Networks
|
Deep Neural Networks (DNNs) have recently shown state of the art performance
on semantic segmentation tasks, however, they still suffer from problems of
poor boundary localization and spatial fragmented predictions. The difficulties
lie in the requirement of making dense predictions from a long path model all
at once since details are hard to keep when data goes through deeper layers.
Instead, in this work, we decompose this difficult task into two relative
simple sub-tasks: seed detection which is required to predict initial
predictions without the need of wholeness and preciseness, and similarity
estimation which measures the possibility of any two nodes belong to the same
class without the need of knowing which class they are. We use one branch
network for one sub-task each, and apply a cascade of random walks base on
hierarchical semantics to approximate a complex diffusion process which
propagates seed information to the whole image according to the estimated
similarities. The proposed DifNet consistently produces improvements over the
baseline models with the same depth and with the equivalent number of
parameters, and also achieves promising performance on Pascal VOC and Pascal
Context dataset. OurDifNet is trained end-to-end without complex loss
functions.
| null |
http://arxiv.org/abs/1805.08015v4
|
http://arxiv.org/pdf/1805.08015v4.pdf
|
NeurIPS 2018 12
|
[
"Peng Jiang",
"Fanglin Gu",
"Yunhai Wang",
"Changhe Tu",
"Baoquan Chen"
] |
[
"Segmentation",
"Semantic Segmentation"
] | 2018-05-21T00:00:00 |
http://papers.nips.cc/paper/7435-difnet-semantic-segmentation-by-diffusion-networks
|
http://papers.nips.cc/paper/7435-difnet-semantic-segmentation-by-diffusion-networks.pdf
|
difnet-semantic-segmentation-by-diffusion-1
| null |
[] |
https://paperswithcode.com/paper/detecting-online-hate-speech-using-context
|
1710.07395
| null | null |
Detecting Online Hate Speech Using Context Aware Models
|
In the wake of a polarizing election, the cyber world is laden with hate
speech. Context accompanying a hate speech text is useful for identifying hate
speech, which however has been largely overlooked in existing datasets and hate
speech detection models. In this paper, we provide an annotated corpus of hate
speech with context information well kept. Then we propose two types of hate
speech detection models that incorporate context information, a logistic
regression model with context features and a neural network model with learning
components for context. Our evaluation shows that both models outperform a
strong baseline by around 3% to 4% in F1 score and combining these two models
further improve the performance by another 7% in F1 score.
|
In the wake of a polarizing election, the cyber world is laden with hate speech.
|
http://arxiv.org/abs/1710.07395v2
|
http://arxiv.org/pdf/1710.07395v2.pdf
|
RANLP 2017 9
|
[
"Lei Gao",
"Ruihong Huang"
] |
[
"Hate Speech Detection"
] | 2017-10-20T00:00:00 |
https://aclanthology.org/R17-1036
|
https://aclanthology.org/R17-1036.pdf
|
detecting-online-hate-speech-using-context-1
| null |
[] |
https://paperswithcode.com/paper/domain-adaptation-using-adversarial-learning
|
1712.03742
| null | null |
Domain Adaptation Using Adversarial Learning for Autonomous Navigation
|
Autonomous navigation has become an increasingly popular machine learning
application. Recent advances in deep learning have also resulted in great
improvements to autonomous navigation. However, prior outdoor autonomous
navigation depends on various expensive sensors or large amounts of real
labeled data which is difficult to acquire and sometimes erroneous. The
objective of this study is to train an autonomous navigation model that uses a
simulator (instead of real labeled data) and an inexpensive monocular camera.
In order to exploit the simulator satisfactorily, our proposed method is based
on domain adaptation with adversarial learning. Specifically, we propose our
model with 1) a dilated residual block in the generator, 2) cycle loss, and 3)
style loss to improve the adversarial learning performance for satisfactory
domain adaptation. In addition, we perform a theoretical analysis that supports
the justification of our proposed method. We present empirical results of
navigation in outdoor courses with various intersections using a commercial
radio controlled car. We observe that our proposed method allows us to learn a
favorable navigation model by generating images with realistic textures. To the
best of our knowledge, this is the first work to apply domain adaptation with
adversarial learning to autonomous navigation in real outdoor environments. Our
proposed method can also be applied to precise image generation or other
robotic tasks.
| null |
http://arxiv.org/abs/1712.03742v6
|
http://arxiv.org/pdf/1712.03742v6.pdf
| null |
[
"Jaeyoon Yoo",
"Yongjun Hong",
"Yung-Kyun Noh",
"Sungroh Yoon"
] |
[
"Autonomous Navigation",
"Domain Adaptation",
"Image Generation"
] | 2017-12-11T00:00:00 | null | null | null | null |
[
{
"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 **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/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#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/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"
}
] |
https://paperswithcode.com/paper/recognizing-explicit-and-implicit-hate-speech
|
1710.07394
| null | null |
Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach
|
In the wake of a polarizing election, social media is laden with hateful
content. To address various limitations of supervised hate speech
classification methods including corpus bias and huge cost of annotation, we
propose a weakly supervised two-path bootstrapping approach for an online hate
speech detection model leveraging large-scale unlabeled data. This system
significantly outperforms hate speech detection systems that are trained in a
supervised manner using manually annotated data. Applying this model on a large
quantity of tweets collected before, after, and on election day reveals
motivations and patterns of inflammatory language.
| null |
http://arxiv.org/abs/1710.07394v2
|
http://arxiv.org/pdf/1710.07394v2.pdf
|
IJCNLP 2017 11
|
[
"Lei Gao",
"Alexis Kuppersmith",
"Ruihong Huang"
] |
[
"General Classification",
"Hate Speech Detection"
] | 2017-10-20T00:00:00 |
https://aclanthology.org/I17-1078
|
https://aclanthology.org/I17-1078.pdf
|
recognizing-explicit-and-implicit-hate-speech-1
| null |
[] |
https://paperswithcode.com/paper/improved-algorithms-for-collaborative-pac
|
1805.08356
| null | null |
Improved Algorithms for Collaborative PAC Learning
|
We study a recent model of collaborative PAC learning where $k$ players with
$k$ different tasks collaborate to learn a single classifier that works for all
tasks. Previous work showed that when there is a classifier that has very small
error on all tasks, there is a collaborative algorithm that finds a single
classifier for all tasks and has $O((\ln (k))^2)$ times the worst-case sample
complexity for learning a single task. In this work, we design new algorithms
for both the realizable and the non-realizable setting, having sample
complexity only $O(\ln (k))$ times the worst-case sample complexity for
learning a single task. The sample complexity upper bounds of our algorithms
match previous lower bounds and in some range of parameters are even better
than previous algorithms that are allowed to output different classifiers for
different tasks.
| null |
http://arxiv.org/abs/1805.08356v2
|
http://arxiv.org/pdf/1805.08356v2.pdf
|
NeurIPS 2018 12
|
[
"Huy L. Nguyen",
"Lydia Zakynthinou"
] |
[
"All",
"PAC learning"
] | 2018-05-22T00:00:00 |
http://papers.nips.cc/paper/7990-improved-algorithms-for-collaborative-pac-learning
|
http://papers.nips.cc/paper/7990-improved-algorithms-for-collaborative-pac-learning.pdf
|
improved-algorithms-for-collaborative-pac-1
| null |
[] |
https://paperswithcode.com/paper/opening-the-black-box-of-deep-learning
|
1805.08355
| null | null |
Opening the black box of deep learning
|
The great success of deep learning shows that its technology contains
profound truth, and understanding its internal mechanism not only has important
implications for the development of its technology and effective application in
various fields, but also provides meaningful insights into the understanding of
human brain mechanism. At present, most of the theoretical research on deep
learning is based on mathematics. This dissertation proposes that the neural
network of deep learning is a physical system, examines deep learning from
three different perspectives: microscopic, macroscopic, and physical world
views, answers multiple theoretical puzzles in deep learning by using physics
principles. For example, from the perspective of quantum mechanics and
statistical physics, this dissertation presents the calculation methods for
convolution calculation, pooling, normalization, and Restricted Boltzmann
Machine, as well as the selection of cost functions, explains why deep learning
must be deep, what characteristics are learned in deep learning, why
Convolutional Neural Networks do not have to be trained layer by layer, and the
limitations of deep learning, etc., and proposes the theoretical direction and
basis for the further development of deep learning now and in the future. The
brilliance of physics flashes in deep learning, we try to establish the deep
learning technology based on the scientific theory of physics.
| null |
http://arxiv.org/abs/1805.08355v1
|
http://arxiv.org/pdf/1805.08355v1.pdf
| null |
[
"Dian Lei",
"Xiaoxiao Chen",
"Jianfei Zhao"
] |
[
"Deep Learning"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-sentence-embeddings-using-recursive
|
1805.08353
| null | null |
Learning sentence embeddings using Recursive Networks
|
Learning sentence vectors that generalise well is a challenging task. In this
paper we compare three methods of learning phrase embeddings: 1) Using LSTMs,
2) using recursive nets, 3) A variant of the method 2 using the POS information
of the phrase. We train our models on dictionary definitions of words to obtain
a reverse dictionary application similar to Felix et al. [1]. To see if our
embeddings can be transferred to a new task we also train and test on the
rotten tomatoes dataset [2]. We train keeping the sentence embeddings fixed as
well as with fine tuning.
|
Learning sentence vectors that generalise well is a challenging task.
|
http://arxiv.org/abs/1805.08353v1
|
http://arxiv.org/pdf/1805.08353v1.pdf
| null |
[
"Anson Bastos"
] |
[
"POS",
"Reverse Dictionary",
"Sentence",
"Sentence Embeddings"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/controlling-personality-based-stylistic
|
1805.08352
| null | null |
Controlling Personality-Based Stylistic Variation with Neural Natural Language Generators
|
Natural language generators for task-oriented dialogue must effectively
realize system dialogue actions and their associated semantics. In many
applications, it is also desirable for generators to control the style of an
utterance. To date, work on task-oriented neural generation has primarily
focused on semantic fidelity rather than achieving stylistic goals, while work
on style has been done in contexts where it is difficult to measure content
preservation. Here we present three different sequence-to-sequence models and
carefully test how well they disentangle content and style. We use a
statistical generator, Personage, to synthesize a new corpus of over 88,000
restaurant domain utterances whose style varies according to models of
personality, giving us total control over both the semantic content and the
stylistic variation in the training data. We then vary the amount of explicit
stylistic supervision given to the three models. We show that our most explicit
model can simultaneously achieve high fidelity to both semantic and stylistic
goals: this model adds a context vector of 36 stylistic parameters as input to
the hidden state of the encoder at each time step, showing the benefits of
explicit stylistic supervision, even when the amount of training data is large.
| null |
http://arxiv.org/abs/1805.08352v1
|
http://arxiv.org/pdf/1805.08352v1.pdf
|
WS 2018 7
|
[
"Shereen Oraby",
"Lena Reed",
"Shubhangi Tandon",
"T. S. Sharath",
"Stephanie Lukin",
"Marilyn Walker"
] |
[] | 2018-05-22T00:00:00 |
https://aclanthology.org/W18-5019
|
https://aclanthology.org/W18-5019.pdf
|
controlling-personality-based-stylistic-1
| null |
[] |
https://paperswithcode.com/paper/a-solvable-high-dimensional-model-of-gan
|
1805.08349
| null | null |
A Solvable High-Dimensional Model of GAN
|
We present a theoretical analysis of the training process for a single-layer GAN fed by high-dimensional input data. The training dynamics of the proposed model at both microscopic and macroscopic scales can be exactly analyzed in the high-dimensional limit. In particular, we prove that the macroscopic quantities measuring the quality of the training process converge to a deterministic process characterized by an ordinary differential equation (ODE), whereas the microscopic states containing all the detailed weights remain stochastic, whose dynamics can be described by a stochastic differential equation (SDE). This analysis provides a new perspective different from recent analyses in the limit of small learning rate, where the microscopic state is always considered deterministic, and the contribution of noise is ignored. From our analysis, we show that the level of the background noise is essential to the convergence of the training process: setting the noise level too strong leads to failure of feature recovery, whereas setting the noise too weak causes oscillation. Although this work focuses on a simple copy model of GAN, we believe the analysis methods and insights developed here would prove useful in the theoretical understanding of other variants of GANs with more advanced training algorithms.
| null |
https://arxiv.org/abs/1805.08349v2
|
https://arxiv.org/pdf/1805.08349v2.pdf
|
NeurIPS 2019 12
|
[
"Chuang Wang",
"Hong Hu",
"Yue M. Lu"
] |
[
"model",
"Vocal Bursts Intensity Prediction"
] | 2018-05-22T00:00:00 |
http://papers.nips.cc/paper/9528-a-solvable-high-dimensional-model-of-gan
|
http://papers.nips.cc/paper/9528-a-solvable-high-dimensional-model-of-gan.pdf
|
a-solvable-high-dimensional-model-of-gan-1
| 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/attacking-visual-language-grounding-with
|
1712.02051
| null | null |
Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning
|
Visual language grounding is widely studied in modern neural image captioning
systems, which typically adopts an encoder-decoder framework consisting of two
principal components: a convolutional neural network (CNN) for image feature
extraction and a recurrent neural network (RNN) for language caption
generation. To study the robustness of language grounding to adversarial
perturbations in machine vision and perception, we propose Show-and-Fool, a
novel algorithm for crafting adversarial examples in neural image captioning.
The proposed algorithm provides two evaluation approaches, which check whether
neural image captioning systems can be mislead to output some randomly chosen
captions or keywords. Our extensive experiments show that our algorithm can
successfully craft visually-similar adversarial examples with randomly targeted
captions or keywords, and the adversarial examples can be made highly
transferable to other image captioning systems. Consequently, our approach
leads to new robustness implications of neural image captioning and novel
insights in visual language grounding.
|
Our extensive experiments show that our algorithm can successfully craft visually-similar adversarial examples with randomly targeted captions or keywords, and the adversarial examples can be made highly transferable to other image captioning systems.
|
http://arxiv.org/abs/1712.02051v2
|
http://arxiv.org/pdf/1712.02051v2.pdf
|
ACL 2018 7
|
[
"Hongge Chen",
"huan zhang",
"Pin-Yu Chen",
"Jin-Feng Yi",
"Cho-Jui Hsieh"
] |
[
"Caption Generation",
"Decoder",
"Image Captioning"
] | 2017-12-06T00:00:00 |
https://aclanthology.org/P18-1241
|
https://aclanthology.org/P18-1241.pdf
|
attacking-visual-language-grounding-with-1
| null |
[] |
https://paperswithcode.com/paper/how-to-solve-moral-conundrums-with
|
1805.08347
| null | null |
How To Solve Moral Conundrums with Computability Theory
|
Various moral conundrums plague population ethics: the Non-Identity Problem, the Procreation Asymmetry, the Repugnant Conclusion, and more. I argue that the aforementioned moral conundrums have a structure neatly accounted for, and solved by, some ideas in computability theory. I introduce a mathematical model based on computability theory and show how previous arguments pertaining to these conundrums fit into the model. This paper proceeds as follows. First, I do a very brief survey of the history of computability theory in moral philosophy. Second, I follow various papers, and show how their arguments fit into, or don't fit into, our model. Third, I discuss the implications of our model to the question why the human race should or should not continue to exist. Finally, I show that our model may be interpreted according to a Confucian-Taoist moral principle.
| null |
https://arxiv.org/abs/1805.08347v3
|
https://arxiv.org/pdf/1805.08347v3.pdf
| null |
[
"Min Baek"
] |
[
"Ethics",
"Philosophy"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/solving-high-dimensional-partial-differential
|
1707.02568
| null | null |
Solving high-dimensional partial differential equations using deep learning
|
Developing algorithms for solving high-dimensional partial differential
equations (PDEs) has been an exceedingly difficult task for a long time, due to
the notoriously difficult problem known as the "curse of dimensionality". This
paper introduces a deep learning-based approach that can handle general
high-dimensional parabolic PDEs. To this end, the PDEs are reformulated using
backward stochastic differential equations and the gradient of the unknown
solution is approximated by neural networks, very much in the spirit of deep
reinforcement learning with the gradient acting as the policy function.
Numerical results on examples including the nonlinear Black-Scholes equation,
the Hamilton-Jacobi-Bellman equation, and the Allen-Cahn equation suggest that
the proposed algorithm is quite effective in high dimensions, in terms of both
accuracy and cost. This opens up new possibilities in economics, finance,
operational research, and physics, by considering all participating agents,
assets, resources, or particles together at the same time, instead of making ad
hoc assumptions on their inter-relationships.
|
Developing algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the notoriously difficult problem known as the "curse of dimensionality".
|
http://arxiv.org/abs/1707.02568v3
|
http://arxiv.org/pdf/1707.02568v3.pdf
| null |
[
"Jiequn Han",
"Arnulf Jentzen",
"Weinan E"
] |
[
"Deep Learning",
"Deep Reinforcement Learning",
"Reinforcement Learning",
"Vocal Bursts Intensity Prediction"
] | 2017-07-09T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/nearest-neighbor-density-functional
|
1805.08342
| null | null |
Nearest neighbor density functional estimation from inverse Laplace transform
|
A new approach to $L_2$-consistent estimation of a general density functional using $k$-nearest neighbor distances is proposed, where the functional under consideration is in the form of the expectation of some function $f$ of the densities at each point. The estimator is designed to be asymptotically unbiased, using the convergence of the normalized volume of a $k$-nearest neighbor ball to a Gamma distribution in the large-sample limit, and naturally involves the inverse Laplace transform of a scaled version of the function $f.$ Some instantiations of the proposed estimator recover existing $k$-nearest neighbor based estimators of Shannon and R\'enyi entropies and Kullback--Leibler and R\'enyi divergences, and discover new consistent estimators for many other functionals such as logarithmic entropies and divergences. The $L_2$-consistency of the proposed estimator is established for a broad class of densities for general functionals, and the convergence rate in mean squared error is established as a function of the sample size for smooth, bounded densities.
|
A new approach to $L_2$-consistent estimation of a general density functional using $k$-nearest neighbor distances is proposed, where the functional under consideration is in the form of the expectation of some function $f$ of the densities at each point.
|
https://arxiv.org/abs/1805.08342v4
|
https://arxiv.org/pdf/1805.08342v4.pdf
| null |
[
"J. Jon Ryu",
"Shouvik Ganguly",
"Young-Han Kim",
"Yung-Kyun Noh",
"Daniel D. Lee"
] |
[] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/constrained-sparse-subspace-clustering-with
|
1805.08183
| null | null |
Constrained Sparse Subspace Clustering with Side-Information
|
Subspace clustering refers to the problem of segmenting high dimensional data
drawn from a union of subspaces into the respective subspaces. In some
applications, partial side-information to indicate "must-link" or "cannot-link"
in clustering is available. This leads to the task of subspace clustering with
side-information. However, in prior work the supervision value of the
side-information for subspace clustering has not been fully exploited. To this
end, in this paper, we present an enhanced approach for constrained subspace
clustering with side-information, termed Constrained Sparse Subspace Clustering
plus (CSSC+), in which the side-information is used not only in the stage of
learning an affinity matrix but also in the stage of spectral clustering.
Moreover, we propose to estimate clustering accuracy based on the partial
side-information and theoretically justify the connection to the ground-truth
clustering accuracy in terms of the Rand index. We conduct experiments on three
cancer gene expression datasets to validate the effectiveness of our proposals.
| null |
http://arxiv.org/abs/1805.08183v2
|
http://arxiv.org/pdf/1805.08183v2.pdf
| null |
[
"Chun-Guang Li",
"Junjian Zhang",
"Jun Guo"
] |
[
"Clustering"
] | 2018-05-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/reducing-parameter-space-for-neural-network
|
1805.08340
| null | null |
Reducing Parameter Space for Neural Network Training
|
For neural networks (NNs) with rectified linear unit (ReLU) or binary activation functions, we show that their training can be accomplished in a reduced parameter space. Specifically, the weights in each neuron can be trained on the unit sphere, as opposed to the entire space, and the threshold can be trained in a bounded interval, as opposed to the real line. We show that the NNs in the reduced parameter space are mathematically equivalent to the standard NNs with parameters in the whole space. The reduced parameter space shall facilitate the optimization procedure for the network training, as the search space becomes (much) smaller. We demonstrate the improved training performance using numerical examples.
|
For neural networks (NNs) with rectified linear unit (ReLU) or binary activation functions, we show that their training can be accomplished in a reduced parameter space.
|
https://arxiv.org/abs/1805.08340v3
|
https://arxiv.org/pdf/1805.08340v3.pdf
| null |
[
"Tong Qin",
"Ling Zhou",
"Dongbin Xiu"
] |
[] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/interpreting-blackbox-models-via-model
|
1705.08504
| null | null |
Interpreting Blackbox Models via Model Extraction
|
Interpretability has become incredibly important as machine learning is
increasingly used to inform consequential decisions. We propose to construct
global explanations of complex, blackbox models in the form of a decision tree
approximating the original model---as long as the decision tree is a good
approximation, then it mirrors the computation performed by the blackbox model.
We devise a novel algorithm for extracting decision tree explanations that
actively samples new training points to avoid overfitting. We evaluate our
algorithm on a random forest to predict diabetes risk and a learned controller
for cart-pole. Compared to several baselines, our decision trees are both
substantially more accurate and equally or more interpretable based on a user
study. Finally, we describe several insights provided by our interpretations,
including a causal issue validated by a physician.
|
Interpretability has become incredibly important as machine learning is increasingly used to inform consequential decisions.
|
http://arxiv.org/abs/1705.08504v6
|
http://arxiv.org/pdf/1705.08504v6.pdf
| null |
[
"Osbert Bastani",
"Carolyn Kim",
"Hamsa Bastani"
] |
[
"model",
"Model extraction"
] | 2017-05-23T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/scalable-centralized-deep-multi-agent
|
1805.08776
| null | null |
Scalable Centralized Deep Multi-Agent Reinforcement Learning via Policy Gradients
|
In this paper, we explore using deep reinforcement learning for problems with
multiple agents. Most existing methods for deep multi-agent reinforcement
learning consider only a small number of agents. When the number of agents
increases, the dimensionality of the input and control spaces increase as well,
and these methods do not scale well. To address this, we propose casting the
multi-agent reinforcement learning problem as a distributed optimization
problem. Our algorithm assumes that for multi-agent settings, policies of
individual agents in a given population live close to each other in parameter
space and can be approximated by a single policy. With this simple assumption,
we show our algorithm to be extremely effective for reinforcement learning in
multi-agent settings. We demonstrate its effectiveness against existing
comparable approaches on co-operative and competitive tasks.
| null |
http://arxiv.org/abs/1805.08776v1
|
http://arxiv.org/pdf/1805.08776v1.pdf
| null |
[
"Arbaaz Khan",
"Clark Zhang",
"Daniel D. Lee",
"Vijay Kumar",
"Alejandro Ribeiro"
] |
[
"Deep Reinforcement Learning",
"Distributed Optimization",
"Multi-agent Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/information-theoretic-limits-for-community
|
1802.06104
| null | null |
Information-theoretic Limits for Community Detection in Network Models
|
We analyze the information-theoretic limits for the recovery of node labels
in several network models. This includes the Stochastic Block Model, the
Exponential Random Graph Model, the Latent Space Model, the Directed
Preferential Attachment Model, and the Directed Small-world Model. For the
Stochastic Block Model, the non-recoverability condition depends on the
probabilities of having edges inside a community, and between different
communities. For the Latent Space Model, the non-recoverability condition
depends on the dimension of the latent space, and how far and spread are the
communities in the latent space. For the Directed Preferential Attachment Model
and the Directed Small-world Model, the non-recoverability condition depends on
the ratio between homophily and neighborhood size. We also consider dynamic
versions of the Stochastic Block Model and the Latent Space Model.
| null |
http://arxiv.org/abs/1802.06104v2
|
http://arxiv.org/pdf/1802.06104v2.pdf
|
NeurIPS 2018 12
|
[
"Chuyang Ke",
"Jean Honorio"
] |
[
"Community Detection",
"Stochastic Block Model"
] | 2018-02-16T00:00:00 |
http://papers.nips.cc/paper/8053-information-theoretic-limits-for-community-detection-in-network-models
|
http://papers.nips.cc/paper/8053-information-theoretic-limits-for-community-detection-in-network-models.pdf
|
information-theoretic-limits-for-community-1
| null |
[] |
https://paperswithcode.com/paper/large-scale-computation-of-means-and-clusters
|
1805.08331
| null | null |
Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport
|
Persistence diagrams (PDs) are now routinely used to summarize the underlying
topology of complex data. Despite several appealing properties, incorporating
PDs in learning pipelines can be challenging because their natural geometry is
not Hilbertian. Indeed, this was recently exemplified in a string of papers
which show that the simple task of averaging a few PDs can be computationally
prohibitive. We propose in this article a tractable framework to carry out
standard tasks on PDs at scale, notably evaluating distances, estimating
barycenters and performing clustering. This framework builds upon a
reformulation of PD metrics as optimal transport (OT) problems. Doing so, we
can exploit recent computational advances: the OT problem on a planar grid,
when regularized with entropy, is convex can be solved in linear time using the
Sinkhorn algorithm and convolutions. This results in scalable computations that
can stream on GPUs. We demonstrate the efficiency of our approach by carrying
out clustering with diagrams metrics on several thousands of PDs, a scale never
seen before in the literature.
| null |
http://arxiv.org/abs/1805.08331v2
|
http://arxiv.org/pdf/1805.08331v2.pdf
|
NeurIPS 2018 12
|
[
"Théo Lacombe",
"Marco Cuturi",
"Steve Oudot"
] |
[
"Clustering"
] | 2018-05-22T00:00:00 |
http://papers.nips.cc/paper/8184-large-scale-computation-of-means-and-clusters-for-persistence-diagrams-using-optimal-transport
|
http://papers.nips.cc/paper/8184-large-scale-computation-of-means-and-clusters-for-persistence-diagrams-using-optimal-transport.pdf
|
large-scale-computation-of-means-and-clusters-1
| null |
[] |
https://paperswithcode.com/paper/guided-feature-transformation-gft-a-neural
|
1805.08329
| null | null |
Guided Feature Transformation (GFT): A Neural Language Grounding Module for Embodied Agents
|
Recently there has been a rising interest in training agents, embodied in
virtual environments, to perform language-directed tasks by deep reinforcement
learning. In this paper, we propose a simple but effective neural language
grounding module for embodied agents that can be trained end to end from
scratch taking raw pixels, unstructured linguistic commands, and sparse rewards
as the inputs. We model the language grounding process as a language-guided
transformation of visual features, where latent sentence embeddings are used as
the transformation matrices. In several language-directed navigation tasks that
feature challenging partial observability and require simple reasoning, our
module significantly outperforms the state of the art. We also release
XWorld3D, an easy-to-customize 3D environment that can potentially be modified
to evaluate a variety of embodied agents.
|
Recently there has been a rising interest in training agents, embodied in virtual environments, to perform language-directed tasks by deep reinforcement learning.
|
http://arxiv.org/abs/1805.08329v2
|
http://arxiv.org/pdf/1805.08329v2.pdf
| null |
[
"Haonan Yu",
"Xiaochen Lian",
"Haichao Zhang",
"Wei Xu"
] |
[
"Deep Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"Sentence",
"Sentence Embeddings"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/adgraph-a-machine-learning-approach-to
|
1805.09155
| null | null |
AdGraph: A Graph-Based Approach to Ad and Tracker Blocking
|
User demand for blocking advertising and tracking online is large and growing. Existing tools, both deployed and described in research, have proven useful, but lack either the completeness or robustness needed for a general solution. Existing detection approaches generally focus on only one aspect of advertising or tracking (e.g. URL patterns, code structure), making existing approaches susceptible to evasion. In this work we present AdGraph, a novel graph-based machine learning approach for detecting advertising and tracking resources on the web. AdGraph differs from existing approaches by building a graph representation of the HTML structure, network requests, and JavaScript behavior of a webpage, and using this unique representation to train a classifier for identifying advertising and tracking resources. Because AdGraph considers many aspects of the context a network request takes place in, it is less susceptible to the single-factor evasion techniques that flummox existing approaches. We evaluate AdGraph on the Alexa top-10K websites, and find that it is highly accurate, able to replicate the labels of human-generated filter lists with 95.33% accuracy, and can even identify many mistakes in filter lists. We implement AdGraph as a modification to Chromium. AdGraph adds only minor overhead to page loading and execution, and is actually faster than stock Chromium on 42% of websites and AdBlock Plus on 78% of websites. Overall, we conclude that AdGraph is both accurate enough and performant enough for online use, breaking comparable or fewer websites than popular filter list based approaches.
|
AdGraph differs from existing approaches by building a graph representation of the HTML structure, network requests, and JavaScript behavior of a webpage, and using this unique representation to train a classifier for identifying advertising and tracking resources.
|
https://arxiv.org/abs/1805.09155v2
|
https://arxiv.org/pdf/1805.09155v2.pdf
| null |
[
"Umar Iqbal",
"Peter Snyder",
"Shitong Zhu",
"Benjamin Livshits",
"Zhiyun Qian",
"Zubair Shafiq"
] |
[
"Blocking"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/verifiable-reinforcement-learning-via-policy
|
1805.08328
| null | null |
Verifiable Reinforcement Learning via Policy Extraction
|
While deep reinforcement learning has successfully solved many challenging
control tasks, its real-world applicability has been limited by the inability
to ensure the safety of learned policies. We propose an approach to verifiable
reinforcement learning by training decision tree policies, which can represent
complex policies (since they are nonparametric), yet can be efficiently
verified using existing techniques (since they are highly structured). The
challenge is that decision tree policies are difficult to train. We propose
VIPER, an algorithm that combines ideas from model compression and imitation
learning to learn decision tree policies guided by a DNN policy (called the
oracle) and its Q-function, and show that it substantially outperforms two
baselines. We use VIPER to (i) learn a provably robust decision tree policy for
a variant of Atari Pong with a symbolic state space, (ii) learn a decision tree
policy for a toy game based on Pong that provably never loses, and (iii) learn
a provably stable decision tree policy for cart-pole. In each case, the
decision tree policy achieves performance equal to that of the original DNN
policy.
|
While deep reinforcement learning has successfully solved many challenging control tasks, its real-world applicability has been limited by the inability to ensure the safety of learned policies.
|
http://arxiv.org/abs/1805.08328v2
|
http://arxiv.org/pdf/1805.08328v2.pdf
|
NeurIPS 2018 12
|
[
"Osbert Bastani",
"Yewen Pu",
"Armando Solar-Lezama"
] |
[
"Deep Reinforcement Learning",
"Imitation Learning",
"Model Compression",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-22T00:00:00 |
http://papers.nips.cc/paper/7516-verifiable-reinforcement-learning-via-policy-extraction
|
http://papers.nips.cc/paper/7516-verifiable-reinforcement-learning-via-policy-extraction.pdf
|
verifiable-reinforcement-learning-via-policy-1
| null |
[] |
https://paperswithcode.com/paper/the-marginal-value-of-adaptive-gradient
|
1705.08292
| null | null |
The Marginal Value of Adaptive Gradient Methods in Machine Learning
|
Adaptive optimization methods, which perform local optimization with a metric
constructed from the history of iterates, are becoming increasingly popular for
training deep neural networks. Examples include AdaGrad, RMSProp, and Adam. We
show that for simple overparameterized problems, adaptive methods often find
drastically different solutions than gradient descent (GD) or stochastic
gradient descent (SGD). We construct an illustrative binary classification
problem where the data is linearly separable, GD and SGD achieve zero test
error, and AdaGrad, Adam, and RMSProp attain test errors arbitrarily close to
half. We additionally study the empirical generalization capability of adaptive
methods on several state-of-the-art deep learning models. We observe that the
solutions found by adaptive methods generalize worse (often significantly
worse) than SGD, even when these solutions have better training performance.
These results suggest that practitioners should reconsider the use of adaptive
methods to train neural networks.
|
Adaptive optimization methods, which perform local optimization with a metric constructed from the history of iterates, are becoming increasingly popular for training deep neural networks.
|
http://arxiv.org/abs/1705.08292v2
|
http://arxiv.org/pdf/1705.08292v2.pdf
|
NeurIPS 2017 12
|
[
"Ashia C. Wilson",
"Rebecca Roelofs",
"Mitchell Stern",
"Nathan Srebro",
"Benjamin Recht"
] |
[
"BIG-bench Machine Learning",
"Binary Classification"
] | 2017-05-23T00:00:00 |
http://papers.nips.cc/paper/7003-the-marginal-value-of-adaptive-gradient-methods-in-machine-learning
|
http://papers.nips.cc/paper/7003-the-marginal-value-of-adaptive-gradient-methods-in-machine-learning.pdf
|
the-marginal-value-of-adaptive-gradient-1
| null |
[
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"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": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"code_snippet_url": "https://github.com/Dawn-Of-Eve/nadir/blob/main/src/nadir/adagrad.py",
"description": "**AdaGrad** is a stochastic optimization method that adapts the learning rate to the parameters. It performs smaller updates for parameters associated with frequently occurring features, and larger updates for parameters associated with infrequently occurring features. In its update rule, Adagrad modifies the general learning rate $\\eta$ at each time step $t$ for every parameter $\\theta\\_{i}$ based on the past gradients for $\\theta\\_{i}$: \r\n\r\n$$ \\theta\\_{t+1, i} = \\theta\\_{t, i} - \\frac{\\eta}{\\sqrt{G\\_{t, ii} + \\epsilon}}g\\_{t, i} $$\r\n\r\nThe benefit of AdaGrad is that it eliminates the need to manually tune the learning rate; most leave it at a default value of $0.01$. Its main weakness is the accumulation of the squared gradients in the denominator. Since every added term is positive, the accumulated sum keeps growing during training, causing the learning rate to shrink and becoming infinitesimally small.\r\n\r\nImage: [Alec Radford](https://twitter.com/alecrad)",
"full_name": "AdaGrad",
"introduced_year": 2011,
"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": "AdaGrad",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/fd8e2064e094f301d910b91a757b860aae3e3116/torch/optim/rmsprop.py#L69-L108",
"description": "**RMSProp** is an unpublished adaptive learning rate optimizer [proposed by Geoff Hinton](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf). The motivation is that the magnitude of gradients can differ for different weights, and can change during learning, making it hard to choose a single global learning rate. RMSProp tackles this by keeping a moving average of the squared gradient and adjusting the weight updates by this magnitude. The gradient updates are performed as:\r\n\r\n$$E\\left[g^{2}\\right]\\_{t} = \\gamma E\\left[g^{2}\\right]\\_{t-1} + \\left(1 - \\gamma\\right) g^{2}\\_{t}$$\r\n\r\n$$\\theta\\_{t+1} = \\theta\\_{t} - \\frac{\\eta}{\\sqrt{E\\left[g^{2}\\right]\\_{t} + \\epsilon}}g\\_{t}$$\r\n\r\nHinton suggests $\\gamma=0.9$, with a good default for $\\eta$ as $0.001$.\r\n\r\nImage: [Alec Radford](https://twitter.com/alecrad)",
"full_name": "RMSProp",
"introduced_year": 2013,
"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": "RMSProp",
"source_title": null,
"source_url": 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/disentangling-controllable-and-uncontrollable
|
1804.06955
| null | null |
Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World
|
We introduce a method to disentangle controllable and uncontrollable factors
of variation by interacting with the world. Disentanglement leads to good
representations and is important when applying deep neural networks (DNNs) in
fields where explanations are required. This study attempts to improve an
existing reinforcement learning (RL) approach to disentangle controllable and
uncontrollable factors of variation, because the method lacks a mechanism to
represent uncontrollable obstacles. To address this problem, we train two DNNs
simultaneously: one that represents the controllable object and another that
represents uncontrollable obstacles. For stable training, we applied a
pretraining approach using a model robust against uncontrollable obstacles.
Simulation experiments demonstrate that the proposed model can disentangle
independently controllable and uncontrollable factors without annotated data.
| null |
http://arxiv.org/abs/1804.06955v2
|
http://arxiv.org/pdf/1804.06955v2.pdf
| null |
[
"Yoshihide Sawada"
] |
[
"Disentanglement",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-04-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/robust-gradient-descent-via-moment-encoding
|
1805.08327
| null | null |
Robust Gradient Descent via Moment Encoding with LDPC Codes
|
This paper considers the problem of implementing large-scale gradient descent
algorithms in a distributed computing setting in the presence of {\em
straggling} processors. To mitigate the effect of the stragglers, it has been
previously proposed to encode the data with an erasure-correcting code and
decode at the master server at the end of the computation. We, instead, propose
to encode the second-moment of the data with a low density parity-check (LDPC)
code. The iterative decoding algorithms for LDPC codes have very low
computational overhead and the number of decoding iterations can be made to
automatically adjust with the number of stragglers in the system. We show that
for a random model for stragglers, the proposed moment encoding based gradient
descent method can be viewed as the stochastic gradient descent method. This
allows us to obtain convergence guarantees for the proposed solution.
Furthermore, the proposed moment encoding based method is shown to outperform
the existing schemes in a real distributed computing setup.
| null |
http://arxiv.org/abs/1805.08327v2
|
http://arxiv.org/pdf/1805.08327v2.pdf
| null |
[
"Raj Kumar Maity",
"Ankit Singh Rawat",
"Arya Mazumdar"
] |
[
"Distributed Computing"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/measurement-wise-occlusion-in-multi-object
|
1805.08324
| null | null |
Measurement-wise Occlusion in Multi-object Tracking
|
Handling object interaction is a fundamental challenge in practical
multi-object tracking, even for simple interactive effects such as one object
temporarily occluding another. We formalize the problem of occlusion in
tracking with two different abstractions. In object-wise occlusion, objects
that are occluded by other objects do not generate measurements. In
measurement-wise occlusion, a previously unstudied approach, all objects may
generate measurements but some measurements may be occluded by others. While
the relative validity of each abstraction depends on the situation and sensor,
measurement-wise occlusion fits into probabilistic multi-object tracking
algorithms with much looser assumptions on object interaction. Its value is
demonstrated by showing that it naturally derives a popular approximation for
lidar tracking, and by an example of visual tracking in image space.
| null |
http://arxiv.org/abs/1805.08324v1
|
http://arxiv.org/pdf/1805.08324v1.pdf
| null |
[
"Michael Motro",
"Joydeep Ghosh"
] |
[
"Multi-Object Tracking",
"Object",
"Object Tracking",
"Visual Tracking"
] | 2018-05-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/ds-mlr-exploiting-double-separability-for
|
1604.04706
| null | null |
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
|
Scaling multinomial logistic regression to datasets with very large number of
data points and classes is challenging. This is primarily because one needs to
compute the log-partition function on every data point. This makes distributing
the computation hard. In this paper, we present a distributed stochastic
gradient descent based optimization method (DS-MLR) for scaling up multinomial
logistic regression problems to massive scale datasets without hitting any
storage constraints on the data and model parameters. Our algorithm exploits
double-separability, an attractive property that allows us to achieve both data
as well as model parallelism simultaneously. In addition, we introduce a
non-blocking and asynchronous variant of our algorithm that avoids
bulk-synchronization. We demonstrate the versatility of DS-MLR to various
scenarios in data and model parallelism, through an extensive empirical study
using several real-world datasets. In particular, we demonstrate the
scalability of DS-MLR by solving an extreme multi-class classification problem
on the Reddit dataset (159 GB data, 358 GB parameters) where, to the best of
our knowledge, no other existing methods apply.
|
Scaling multinomial logistic regression to datasets with very large number of data points and classes is challenging.
|
http://arxiv.org/abs/1604.04706v7
|
http://arxiv.org/pdf/1604.04706v7.pdf
| null |
[
"Parameswaran Raman",
"Sriram Srinivasan",
"Shin Matsushima",
"Xinhua Zhang",
"Hyokun Yun",
"S. V. N. Vishwanathan"
] |
[
"Blocking",
"Multi-class Classification",
"regression"
] | 2016-04-16T00:00:00 | null | null | null | 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/extreme-stochastic-variational-inference
|
1605.09499
| null | null |
Extreme Stochastic Variational Inference: Distributed and Asynchronous
|
Stochastic variational inference (SVI), the state-of-the-art algorithm for
scaling variational inference to large-datasets, is inherently serial.
Moreover, it requires the parameters to fit in the memory of a single
processor; this is problematic when the number of parameters is in billions. In
this paper, we propose extreme stochastic variational inference (ESVI), an
asynchronous and lock-free algorithm to perform variational inference for
mixture models on massive real world datasets. ESVI overcomes the limitations
of SVI by requiring that each processor only access a subset of the data and a
subset of the parameters, thus providing data and model parallelism
simultaneously. We demonstrate the effectiveness of ESVI by running Latent
Dirichlet Allocation (LDA) on UMBC-3B, a dataset that has a vocabulary of 3
million and a token size of 3 billion. In our experiments, we found that ESVI
not only outperforms VI and SVI in wallclock-time, but also achieves a better
quality solution. In addition, we propose a strategy to speed up computation
and save memory when fitting large number of topics.
| null |
http://arxiv.org/abs/1605.09499v9
|
http://arxiv.org/pdf/1605.09499v9.pdf
| null |
[
"Jiong Zhang",
"Parameswaran Raman",
"Shihao Ji",
"Hsiang-Fu Yu",
"S. V. N. Vishwanathan",
"Inderjit S. Dhillon"
] |
[
"Variational Inference"
] | 2016-05-31T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/a-new-finitely-controllable-class-of-tuple
|
1805.09157
| null | null |
A New Finitely Controllable Class of Tuple Generating Dependencies: The Triangularly-Guarded Class
|
In this paper we introduce a new class of tuple-generating dependencies
(TGDs) called triangularly-guarded (TG) TGDs. We show that conjunctive query
answering under this new class of TGDs is decidable since this new class of
TGDs also satisfies the finite controllability (FC) property. We further show
that this new class strictly contains some other decidable classes such as
weak-acyclic, guarded, sticky and shy. In this sense, the class TG provides a
unified representation of all these aforementioned classes of TGDs.
| null |
http://arxiv.org/abs/1805.09157v1
|
http://arxiv.org/pdf/1805.09157v1.pdf
| null |
[
"Vernon Asuncion",
"Yan Zhang"
] |
[] | 2018-05-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/linear-time-constituency-parsing-with-rnns
|
1805.06995
| null | null |
Linear-Time Constituency Parsing with RNNs and Dynamic Programming
|
Recently, span-based constituency parsing has achieved competitive accuracies
with extremely simple models by using bidirectional RNNs to model "spans".
However, the minimal span parser of Stern et al (2017a) which holds the current
state of the art accuracy is a chart parser running in cubic time, $O(n^3)$,
which is too slow for longer sentences and for applications beyond sentence
boundaries such as end-to-end discourse parsing and joint sentence boundary
detection and parsing. We propose a linear-time constituency parser with RNNs
and dynamic programming using graph-structured stack and beam search, which
runs in time $O(n b^2)$ where $b$ is the beam size. We further speed this up to
$O(n b\log b)$ by integrating cube pruning. Compared with chart parsing
baselines, this linear-time parser is substantially faster for long sentences
on the Penn Treebank and orders of magnitude faster for discourse parsing, and
achieves the highest F1 accuracy on the Penn Treebank among single model
end-to-end systems.
| null |
http://arxiv.org/abs/1805.06995v2
|
http://arxiv.org/pdf/1805.06995v2.pdf
|
ACL 2018 7
|
[
"Juneki Hong",
"Liang Huang"
] |
[
"Boundary Detection",
"Constituency Parsing",
"Discourse Parsing",
"Sentence"
] | 2018-05-17T00:00:00 |
https://aclanthology.org/P18-2076
|
https://aclanthology.org/P18-2076.pdf
|
linear-time-constituency-parsing-with-rnns-1
| null |
[
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. \r\nApproaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.",
"full_name": "SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings",
"introduced_year": 2000,
"main_collection": null,
"name": "SPEED",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/two-hilbert-schemes-in-computer-vision
|
1707.09332
| null | null |
Two Hilbert schemes in computer vision
|
We study multiview moduli problems that arise in computer vision. We show that these moduli spaces are always smooth and irreducible, in both the calibrated and uncalibrated cases, for any number of views. We also show that these moduli spaces always admit open immersions into Hilbert schemes for more than two views, extending and refining work of Aholt-Sturmfels-Thomas. We use these moduli spaces to study and extend the classical twisted pair covering of the essential variety.
| null |
https://arxiv.org/abs/1707.09332v6
|
https://arxiv.org/pdf/1707.09332v6.pdf
| null |
[
"Max Lieblich",
"Lucas Van Meter"
] |
[
"Vocal Bursts Valence Prediction"
] | 2017-07-28T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/self-attention-generative-adversarial
|
1805.08318
| null | null |
Self-Attention Generative Adversarial Networks
|
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.
|
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.
|
https://arxiv.org/abs/1805.08318v2
|
https://arxiv.org/pdf/1805.08318v2.pdf
|
arXiv 2018 5
|
[
"Han Zhang",
"Ian Goodfellow",
"Dimitris Metaxas",
"Augustus Odena"
] |
[
"Conditional Image Generation",
"Generative Adversarial Network",
"Image Generation"
] | 2018-05-21T00:00:00 | null | null |
self-attention-generative-adversarial-1
| null |
[
{
"code_snippet_url": "https://github.com/lim0606/pytorch-geometric-gan/blob/eb84feb5cae1d6963c075aa6fb4c0c3a18eeec41/main.py#L303",
"description": "The **GAN Hinge Loss** is a hinge loss based loss function for [generative adversarial networks](https://paperswithcode.com/methods/category/generative-adversarial-networks):\r\n\r\n$$ L\\_{D} = -\\mathbb{E}\\_{\\left(x, y\\right)\\sim{p}\\_{data}}\\left[\\min\\left(0, -1 + D\\left(x, y\\right)\\right)\\right] -\\mathbb{E}\\_{z\\sim{p\\_{z}}, y\\sim{p\\_{data}}}\\left[\\min\\left(0, -1 - D\\left(G\\left(z\\right), y\\right)\\right)\\right] $$\r\n\r\n$$ L\\_{G} = -\\mathbb{E}\\_{z\\sim{p\\_{z}}, y\\sim{p\\_{data}}}D\\left(G\\left(z\\right), y\\right) $$",
"full_name": "GAN Hinge 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 Hinge Loss",
"source_title": "Geometric GAN",
"source_url": "http://arxiv.org/abs/1705.02894v2"
},
{
"code_snippet_url": "",
"description": "To communicate or get human at Expedia, the quickest option is typically to call their customer service at ＋１－８８８－８２９－０８８１ ｏｒ ＋１（８０５） ３３０ （４０５６）. You can also use the live chat feature on their website or app, or contact them via social media. For urgent requests like new booking, flight changes, cancellations, or refunds, calling Expedia’s customer support at ＋１－８８８－８２９－０８８１ ｏｒ ＋１（８０５） ３３０ （４０５６） is the most efficient way to get help.",
"full_name": "Six Ways To Communicate To Someone At Expedia Via Phone And Email's.",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "If you're looking to get in touch with American Airlines fast, ☎️+1-801-(855)-(5905)or +1-804-853-9001✅ there are\r\nseveral efficient ways to reach their customer service team. The quickest method is to dial ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. American’s phone service ensures that you can speak with a live\r\nrepresentative promptly to resolve any issues or queries regarding your booking, reservation,\r\nor any changes, such as name corrections or ticket cancellations.",
"name": "Attention Mechanisms",
"parent": "Attention"
},
"name": "Six Ways To Communicate To Someone At Expedia Via Phone And Email's.",
"source_title": "Effective Approaches to Attention-based Neural Machine Translation",
"source_url": "http://arxiv.org/abs/1508.04025v5"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.com/method/rmsprop) and [SGD w/th Momentum](https://paperswithcode.com/method/sgd-with-momentum). The optimizer is designed to be appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. \r\n\r\nThe weight updates are performed as:\r\n\r\n$$ w_{t} = w_{t-1} - \\eta\\frac{\\hat{m}\\_{t}}{\\sqrt{\\hat{v}\\_{t}} + \\epsilon} $$\r\n\r\nwith\r\n\r\n$$ \\hat{m}\\_{t} = \\frac{m_{t}}{1-\\beta^{t}_{1}} $$\r\n\r\n$$ \\hat{v}\\_{t} = \\frac{v_{t}}{1-\\beta^{t}_{2}} $$\r\n\r\n$$ m_{t} = \\beta_{1}m_{t-1} + (1-\\beta_{1})g_{t} $$\r\n\r\n$$ v_{t} = \\beta_{2}v_{t-1} + (1-\\beta_{2})g_{t}^{2} $$\r\n\r\n\r\n$ \\eta $ is the step size/learning rate, around 1e-3 in the original paper. $ \\epsilon $ is a small number, typically 1e-8 or 1e-10, to prevent dividing by zero. $ \\beta_{1} $ and $ \\beta_{2} $ are forgetting parameters, with typical values 0.9 and 0.999, respectively.",
"full_name": "Adam",
"introduced_year": 2000,
"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": "Adam",
"source_title": "Adam: A Method for Stochastic Optimization",
"source_url": "http://arxiv.org/abs/1412.6980v9"
},
{
"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": 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": "",
"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/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/christiancosgrove/pytorch-spectral-normalization-gan/blob/12dcf945a6359301d63d1e0da3708cd0f0590b19/spectral_normalization.py#L14",
"description": "**Spectral Normalization** is a normalization technique used for generative adversarial networks, used to stabilize training of the discriminator. Spectral normalization has the convenient property that the Lipschitz constant is the only hyper-parameter to be tuned.\r\n\r\nIt controls the Lipschitz constant of the discriminator $f$ by constraining the spectral norm of each layer $g : \\textbf{h}\\_{in} \\rightarrow \\textbf{h}_{out}$. The Lipschitz norm $\\Vert{g}\\Vert\\_{\\text{Lip}}$ is equal to $\\sup\\_{\\textbf{h}}\\sigma\\left(\\nabla{g}\\left(\\textbf{h}\\right)\\right)$, where $\\sigma\\left(a\\right)$ is the spectral norm of the matrix $A$ ($L\\_{2}$ matrix norm of $A$):\r\n\r\n$$ \\sigma\\left(a\\right) = \\max\\_{\\textbf{h}:\\textbf{h}\\neq{0}}\\frac{\\Vert{A\\textbf{h}}\\Vert\\_{2}}{\\Vert\\textbf{h}\\Vert\\_{2}} = \\max\\_{\\Vert\\textbf{h}\\Vert\\_{2}\\leq{1}}{\\Vert{A\\textbf{h}}\\Vert\\_{2}} $$\r\n\r\nwhich is equivalent to the largest singular value of $A$. Therefore for a [linear layer](https://paperswithcode.com/method/linear-layer) $g\\left(\\textbf{h}\\right) = W\\textbf{h}$ the norm is given by $\\Vert{g}\\Vert\\_{\\text{Lip}} = \\sup\\_{\\textbf{h}}\\sigma\\left(\\nabla{g}\\left(\\textbf{h}\\right)\\right) = \\sup\\_{\\textbf{h}}\\sigma\\left(W\\right) = \\sigma\\left(W\\right) $. Spectral normalization normalizes the spectral norm of the weight matrix $W$ so it satisfies the Lipschitz constraint $\\sigma\\left(W\\right) = 1$:\r\n\r\n$$ \\bar{W}\\_{\\text{SN}}\\left(W\\right) = W / \\sigma\\left(W\\right) $$",
"full_name": "Spectral 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": "Spectral Normalization",
"source_title": "Spectral Normalization for Generative Adversarial Networks",
"source_url": "http://arxiv.org/abs/1802.05957v1"
},
{
"code_snippet_url": "",
"description": "How do I cancel a reservation on Expedia? \r\n\r\n+1^888^829^0881° oR +1^888^829^0881 – Need to cancel your Expedia reservation quickly and without hassle? This step-by-step guide will show you how to do it with ease. +1^888^829^0881° oR +1^888^829^0881 – Expedia makes it relatively simple to cancel most bookings directly through their website or app. Whether you booked a hotel, flight, rental car, or vacation package, knowing the cancellation process is essential. +1^888^829^0881° oR +1^888^829^0881 – First, log in to your Expedia account using your email and password. Once you're in, navigate to “My Trips” at the top of the screen. This section lists all your past and upcoming reservations. +1^888^829^0881° oR +1^888^829^0881 – Locate the reservation you want to cancel and click on it. You’ll see all the details, including the cancellation policy. Make sure to read the policy carefully, as not all bookings are refundable. +1^888^829^0881° oR +1^888^829^0881 – Click the “Cancel” button next to your reservation. You may be prompted to confirm the cancellation. Once confirmed, you will receive an email regarding your cancellation status. +1^888^829^0881° oR +1^888^829^0881 – If your booking is refundable, you’ll get the money back in your original payment method within 7–10 business days, depending on your bank or credit card issuer. +1^888^829^0881° oR +1^888^829^0881 – For flights, cancellation rules vary by airline. Some may offer flight credits instead of cash refunds. Review your flight provider's policy through the Expedia portal. +1^888^829^0881° oR +1^888^829^0881 – Hotel cancellations are generally more flexible, especially for “free cancellation” listings. Just ensure you cancel before the deadline stated in the policy. +1^888^829^0881° oR +1^888^829^0881 – If you booked a vacation package, the process is similar, but cancellation fees may apply based on the individual elements (flight, hotel, etc.) of the package. +1^888^829^0881° oR +1^888^829^0881 – If you’re facing issues online or need help with a non-refundable booking, it’s best to speak to a live Expedia representative. +1^888^829^0881° oR +1^888^829^0881 – Dial Expedia customer support at +1^888^829^0881° oR +1^888^829^0881. They can assist you with cancellations, rebooking, or disputes. +1^888^829^0881° oR +1^888^829^0881 – When calling, have your itinerary number, travel dates, and email address ready. This will speed up the process. +1^888^829^0881° oR +1^888^829^0881 – If you purchased trip protection or travel insurance, ask about filing a claim. Some policies allow cancellation for any reason (CFAR). +1^888^829^0881° oR +1^888^829^0881 – Avoid waiting until the last minute. The sooner you cancel, the more likely you'll receive a refund or avoid cancellation penalties. Pro Tips for Hassle-Free Cancellation +1^888^829^0881° oR +1^888^829^0881 – Always book refundable options whenever possible. These give you the most flexibility if plans change. +1^888^829^0881° oR +1^888^829^0881 – Double-check time zones on cancellation deadlines. Some hotels use local time, which can impact your eligibility. +1^888^829^0881° oR +1^888^829^0881 – Keep a copy of your cancellation confirmation email. It acts as proof in case of any disputes. +1^888^829^0881° oR +1^888^829^0881 – Don’t forget to cancel directly with the hotel or airline too if Expedia’s system doesn’t allow same-day cancellations. +1^888^829^0881° oR +1^888^829^0881 – If a refund takes longer than expected, follow up using the number +1^888^829^0881° oR +1^888^829^0881 for a status update. +1^888^829^0881° oR +1^888^829^0881 – Canceling a reservation on Expedia is straightforward if you follow the proper steps and act within the allowed time frame. +1^888^829^0881° oR +1^888^829^0881 – Use your Expedia account to view your trips and cancel bookings online, or call +1^888^829^0881° oR +1^888^829^0881 if you need personal assistance. +1^888^829^0881° oR +1^888^829^0881 – Read the cancellation terms before booking and again before you cancel to ensure you know what fees or refunds apply. +1^888^829^0881° oR +1^888^829^0881 – With the right knowledge and support, managing your Expedia reservation is stress-free and efficient. +1^888^829^0881° oR +1^888^829^0881 – Save or bookmark this guide for future travel planning and cancellation support.",
"full_name": "((Reservation@Faqs))How do I cancel a reservation on Expedia?",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Attention Modules** refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.",
"name": "Attention Modules",
"parent": "Attention"
},
"name": "((Reservation@Faqs))How do I cancel a reservation on Expedia?",
"source_title": "Self-Attention Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1805.08318v2"
},
{
"code_snippet_url": "https://github.com/heykeetae/Self-Attention-GAN/blob/master/sagan_models.py",
"description": "The **Self-Attention Generative Adversarial Network**, or **SAGAN**, allows for attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other.",
"full_name": "Self-Attention GAN",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Adversarial Networks (GANs)** are a type of generative model that use two networks, a generator to generate images and a discriminator to discriminate between real and fake, to train a model that approximates the distribution of the data. Below you can find a continuously updating list of GANs.",
"name": "Generative Adversarial Networks",
"parent": "Generative Models"
},
"name": "SAGAN",
"source_title": "Self-Attention Generative Adversarial Networks",
"source_url": "https://arxiv.org/abs/1805.08318v2"
},
{
"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/learning-long-range-spatial-dependencies-with-1
|
1805.08315
| null | null |
Learning long-range spatial dependencies with horizontal gated-recurrent units
|
Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a variety of visual recognition tasks. Here, however, we show that these neural networks and their recent extensions struggle in recognition tasks where co-dependent visual features must be detected over long spatial ranges. We introduce the horizontal gated-recurrent unit (hGRU) to learn intrinsic horizontal connections -- both within and across feature columns. We demonstrate that a single hGRU layer matches or outperforms all tested feedforward hierarchical baselines including state-of-the-art architectures which have orders of magnitude more free parameters. We further discuss the biological plausibility of the hGRU in comparison to anatomical data from the visual cortex as well as human behavioral data on a classic contour detection task.
|
As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a variety of visual recognition tasks.
|
https://arxiv.org/abs/1805.08315v4
|
https://arxiv.org/pdf/1805.08315v4.pdf
|
NeurIPS 2018
|
[
"Drew Linsley",
"Junkyung Kim",
"Vijay Veerabadran",
"Thomas Serre"
] |
[
"Contour Detection"
] | 2018-05-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/learning-safe-policies-with-expert-guidance
|
1805.08313
| null | null |
Learning Safe Policies with Expert Guidance
|
We propose a framework for ensuring safe behavior of a reinforcement learning
agent when the reward function may be difficult to specify. In order to do
this, we rely on the existence of demonstrations from expert policies, and we
provide a theoretical framework for the agent to optimize in the space of
rewards consistent with its existing knowledge. We propose two methods to solve
the resulting optimization: an exact ellipsoid-based method and a method in the
spirit of the "follow-the-perturbed-leader" algorithm. Our experiments
demonstrate the behavior of our algorithm in both discrete and continuous
problems. The trained agent safely avoids states with potential negative
effects while imitating the behavior of the expert in the other states.
| null |
http://arxiv.org/abs/1805.08313v2
|
http://arxiv.org/pdf/1805.08313v2.pdf
|
NeurIPS 2018 12
|
[
"Jessie Huang",
"Fa Wu",
"Doina Precup",
"Yang Cai"
] |
[
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-21T00:00:00 |
http://papers.nips.cc/paper/8124-learning-safe-policies-with-expert-guidance
|
http://papers.nips.cc/paper/8124-learning-safe-policies-with-expert-guidance.pdf
|
learning-safe-policies-with-expert-guidance-1
| null |
[] |
https://paperswithcode.com/paper/agilenet-lightweight-dictionary-based-few
|
1805.08311
| null | null |
AgileNet: Lightweight Dictionary-based Few-shot Learning
|
The success of deep learning models is heavily tied to the use of massive
amount of labeled data and excessively long training time. With the emergence
of intelligent edge applications that use these models, the critical challenge
is to obtain the same inference capability on a resource-constrained device
while providing adaptability to cope with the dynamic changes in the data. We
propose AgileNet, a novel lightweight dictionary-based few-shot learning
methodology which provides reduced complexity deep neural network for efficient
execution at the edge while enabling low-cost updates to capture the dynamics
of the new data. Evaluations of state-of-the-art few-shot learning benchmarks
demonstrate the superior accuracy of AgileNet compared to prior arts.
Additionally, AgileNet is the first few-shot learning approach that prevents
model updates by eliminating the knowledge obtained from the primary training.
This property is ensured through the dictionaries learned by our novel
end-to-end structured decomposition, which also reduces the memory footprint
and computation complexity to match the edge device constraints.
| null |
http://arxiv.org/abs/1805.08311v1
|
http://arxiv.org/pdf/1805.08311v1.pdf
| null |
[
"Mohammad Ghasemzadeh",
"Fang Lin",
"Bita Darvish Rouhani",
"Farinaz Koushanfar",
"Ke Huang"
] |
[
"Few-Shot Learning"
] | 2018-05-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/axtrain-hardware-oriented-neural-network
|
1805.08309
| null | null |
AxTrain: Hardware-Oriented Neural Network Training for Approximate Inference
|
The intrinsic error tolerance of neural network (NN) makes approximate
computing a promising technique to improve the energy efficiency of NN
inference. Conventional approximate computing focuses on balancing the
efficiency-accuracy trade-off for existing pre-trained networks, which can lead
to suboptimal solutions. In this paper, we propose AxTrain, a hardware-oriented
training framework to facilitate approximate computing for NN inference.
Specifically, AxTrain leverages the synergy between two orthogonal
methods---one actively searches for a network parameters distribution with high
error tolerance, and the other passively learns resilient weights by
numerically incorporating the noise distributions of the approximate hardware
in the forward pass during the training phase. Experimental results from
various datasets with near-threshold computing and approximation multiplication
strategies demonstrate AxTrain's ability to obtain resilient neural network
parameters and system energy efficiency improvement.
| null |
http://arxiv.org/abs/1805.08309v1
|
http://arxiv.org/pdf/1805.08309v1.pdf
| null |
[
"Xin He",
"Liu Ke",
"Wenyan Lu",
"Guihai Yan",
"Xuan Zhang"
] |
[] | 2018-05-21T00:00:00 | null | null | null | null |
[] |
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