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https://paperswithcode.com/paper/comparison-of-vca-and-gaee-algorithms-for
|
1805.10644
| null | null |
Comparison of VCA and GAEE algorithms for Endmember Extraction
|
Endmember Extraction is a critical step in hyperspectral image analysis and
classification. It is an useful method to decompose a mixed spectrum into a
collection of spectra and their corresponding proportions. In this paper, we
solve a linear endmember extraction problem as an evolutionary optimization
task, maximizing the Simplex Volume in the endmember space. We propose a
standard genetic algorithm and a variation with In Vitro Fertilization module
(IVFm) to find the best solutions and compare the results with the state-of-art
Vertex Component Analysis (VCA) method and the traditional algorithms Pixel
Purity Index (PPI) and N-FINDR. The experimental results on real and synthetic
hyperspectral data confirms the overcome in performance and accuracy of the
proposed approaches over the mentioned algorithms.
|
Endmember Extraction is a critical step in hyperspectral image analysis and classification.
|
http://arxiv.org/abs/1805.10644v1
|
http://arxiv.org/pdf/1805.10644v1.pdf
| null |
[
"Douglas Winston. R. S.",
"Gustavo T. Laureano",
"Celso G. Camilo Jr"
] |
[
"General Classification",
"Hyperspectral image analysis"
] | 2018-05-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fast-k-means-clustering-with-anderson
|
1805.10638
| null | null |
Fast K-Means Clustering with Anderson Acceleration
|
We propose a novel method to accelerate Lloyd's algorithm for K-Means
clustering. Unlike previous acceleration approaches that reduce computational
cost per iterations or improve initialization, our approach is focused on
reducing the number of iterations required for convergence. This is achieved by
treating the assignment step and the update step of Lloyd's algorithm as a
fixed-point iteration, and applying Anderson acceleration, a well-established
technique for accelerating fixed-point solvers. Classical Anderson acceleration
utilizes m previous iterates to find an accelerated iterate, and its
performance on K-Means clustering can be sensitive to choice of m and the
distribution of samples. We propose a new strategy to dynamically adjust the
value of m, which achieves robust and consistent speedups across different
problem instances. Our method complements existing acceleration techniques, and
can be combined with them to achieve state-of-the-art performance. We perform
extensive experiments to evaluate the performance of the proposed method, where
it outperforms other algorithms in 106 out of 120 test cases, and the mean
decrease ratio of computational time is more than 33%.
| null |
http://arxiv.org/abs/1805.10638v1
|
http://arxiv.org/pdf/1805.10638v1.pdf
| null |
[
"Juyong Zhang",
"Yuxin Yao",
"Yue Peng",
"Hao Yu",
"Bailin Deng"
] |
[
"Clustering"
] | 2018-05-27T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://cryptoabout.info",
"description": "**k-Means Clustering** is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$ different centroids {$\\mu\\left(1\\right),\\ldots,\\mu\\left(k\\right)$} to different values, then alternating between two steps until convergence:\r\n\r\n(i) each training example is assigned to cluster $i$ where $i$ is the index of the nearest centroid $\\mu^{(i)}$\r\n\r\n(ii) each centroid $\\mu^{(i)}$ is updated to the mean of all training examples $x^{(j)}$ assigned to cluster $i$.\r\n\r\nText Source: Deep Learning, Goodfellow et al\r\n\r\nImage Source: [scikit-learn](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html)",
"full_name": "k-Means Clustering",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Clustering** methods cluster a dataset so that similar datapoints are located in the same group. Below you can find a continuously updating list of clustering methods.",
"name": "Clustering",
"parent": null
},
"name": "k-Means Clustering",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/contextual-graph-markov-model-a-deep-and
|
1805.10636
| null | null |
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
|
We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data. It founds on a constructive methodology to build a deep architecture comprising layers of probabilistic models that learn to encode the structured information in an incremental fashion. Context is diffused in an efficient and scalable way across the graph vertexes and edges. The resulting graph encoding is used in combination with discriminative models to address structure classification benchmarks.
|
We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data.
|
https://arxiv.org/abs/1805.10636v2
|
https://arxiv.org/pdf/1805.10636v2.pdf
|
ICML 2018 7
|
[
"Davide Bacciu",
"Federico Errica",
"Alessio Micheli"
] |
[
"General Classification"
] | 2018-05-27T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2084
|
http://proceedings.mlr.press/v80/bacciu18a/bacciu18a.pdf
|
contextual-graph-markov-model-a-deep-and-1
| null |
[
{
"code_snippet_url": "",
"description": "Contextual Graph Markov Model (CGMM) is an approach combining ideas from generative models and neural networks for the processing of graph data. It founds on a constructive methodology to build a deep architecture comprising layers of probabilistic models that learn to encode the structured information in an incremental fashion. Context is diffused in an efficient and scalable way across the graph vertexes and edges. The resulting graph encoding is used in combination with discriminative models to address structure classification benchmarks.\r\n\r\nDescription and image from: [Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing](https://arxiv.org/pdf/1805.10636.pdf)",
"full_name": "Contextual Graph Markov Model",
"introduced_year": 2000,
"main_collection": {
"area": "Graphs",
"description": "The Graph Methods include neural network architectures for learning on graphs with prior structure information, popularly called as Graph Neural Networks (GNNs).\r\n\r\nRecently, deep learning approaches are being extended to work on graph-structured data, giving rise to a series of graph neural networks addressing different challenges. Graph neural networks are particularly useful in applications where data are generated from non-Euclidean domains and represented as graphs with complex relationships. \r\n\r\nSome tasks where GNNs are widely used include [node classification](https://paperswithcode.com/task/node-classification), [graph classification](https://paperswithcode.com/task/graph-classification), [link prediction](https://paperswithcode.com/task/link-prediction), and much more. \r\n\r\nIn the taxonomy presented by [Wu et al. (2019)](https://paperswithcode.com/paper/a-comprehensive-survey-on-graph-neural), graph neural networks can be divided into four categories: **recurrent graph neural networks**, **convolutional graph neural networks**, **graph autoencoders**, and **spatial-temporal graph neural networks**.\r\n\r\nImage source: [A Comprehensive Survey on Graph NeuralNetworks](https://arxiv.org/pdf/1901.00596.pdf)",
"name": "Graph Models",
"parent": null
},
"name": "CGMM",
"source_title": "Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing",
"source_url": "https://arxiv.org/abs/1805.10636v2"
}
] |
https://paperswithcode.com/paper/unsupervised-learning-of-mixture-models-with
|
1804.02744
| null | null |
Unsupervised Learning of GMM with a Uniform Background Component
|
Gaussian Mixture Models are one of the most studied and mature models in unsupervised learning. However, outliers are often present in the data and could influence the cluster estimation. In this paper, we study a new model that assumes that data comes from a mixture of a number of Gaussians as well as a uniform ``background'' component assumed to contain outliers and other non-interesting observations. We develop a novel method based on robust loss minimization that performs well in clustering such GMM with a uniform background. We give theoretical guarantees for our clustering algorithm to obtain best clustering results with high probability. Besides, we show that the result of our algorithm does not depend on initialization or local optima, and the parameter tuning is an easy task. By numeric simulations, we demonstrate that our algorithm enjoys high accuracy and achieves the best clustering results given a large enough sample size. Finally, experimental comparisons with typical clustering methods on real datasets witness the potential of our algorithm in real applications.
|
Gaussian Mixture Models are one of the most studied and mature models in unsupervised learning.
|
https://arxiv.org/abs/1804.02744v4
|
https://arxiv.org/pdf/1804.02744v4.pdf
| null |
[
"Sida Liu",
"Adrian Barbu"
] |
[
"Clustering"
] | 2018-04-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-simple-riemannian-manifold-network-for
|
1805.10628
| null | null |
A Simple Riemannian Manifold Network for Image Set Classification
|
In the domain of image-set based classification, a considerable advance has
been made by representing original image sets as covariance matrices which
typical lie in a Riemannian manifold. Specifically, it is a Symmetric Positive
Definite (SPD) manifold. Traditional manifold learning methods inevitably have
the property of high computational complexity or weak performance of the
feature representation. In order to overcome these limitations, we propose a
very simple Riemannian manifold network for image set classification. Inspired
by deep learning architectures, we design a fully connected layer to generate
more novel, more powerful SPD matrices. However we exploit the rectifying layer
to prevent the input SPD matrices from being singular. We also introduce a
non-linear learning of the proposed network with an innovative objective
function. Furthermore we devise a pooling layer to further reduce the
redundancy of the input SPD matrices, and the log-map layer to project the SPD
manifold to the Euclidean space. For learning the connection weights between
the input layer and the fully connected layer, we use Two-directional
two-dimensional Principal Component Analysis ((2D)2PCA) algorithm. The proposed
Riemannian manifold network (RieMNet) avoids complex computing and can be built
and trained extremely easy and efficient. We have also developed a deep version
of RieMNet, named as DRieMNet. The proposed RieMNet and DRieMNet are evaluated
on three tasks: video-based face recognition, set-based object categorization,
and set-based cell identification. Extensive experimental results show the
superiority of our method over the state-of-the-art.
| null |
http://arxiv.org/abs/1805.10628v2
|
http://arxiv.org/pdf/1805.10628v2.pdf
| null |
[
"Rui Wang",
"Xiao-Jun Wu",
"Josef Kittler"
] |
[
"Classification",
"Face Recognition",
"General Classification",
"Object Categorization"
] | 2018-05-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/reliability-and-learnability-of-human-bandit
|
1805.10627
| null | null |
Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning
|
We present a study on reinforcement learning (RL) from human bandit feedback
for sequence-to-sequence learning, exemplified by the task of bandit neural
machine translation (NMT). We investigate the reliability of human bandit
feedback, and analyze the influence of reliability on the learnability of a
reward estimator, and the effect of the quality of reward estimates on the
overall RL task. Our analysis of cardinal (5-point ratings) and ordinal
(pairwise preferences) feedback shows that their intra- and inter-annotator
$\alpha$-agreement is comparable. Best reliability is obtained for standardized
cardinal feedback, and cardinal feedback is also easiest to learn and
generalize from. Finally, improvements of over 1 BLEU can be obtained by
integrating a regression-based reward estimator trained on cardinal feedback
for 800 translations into RL for NMT. This shows that RL is possible even from
small amounts of fairly reliable human feedback, pointing to a great potential
for applications at larger scale.
|
We present a study on reinforcement learning (RL) from human bandit feedback for sequence-to-sequence learning, exemplified by the task of bandit neural machine translation (NMT).
|
http://arxiv.org/abs/1805.10627v3
|
http://arxiv.org/pdf/1805.10627v3.pdf
|
ACL 2018 7
|
[
"Julia Kreutzer",
"Joshua Uyheng",
"Stefan Riezler"
] |
[
"Machine Translation",
"NMT",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)",
"Translation"
] | 2018-05-27T00:00:00 |
https://aclanthology.org/P18-1165
|
https://aclanthology.org/P18-1165.pdf
|
reliability-and-learnability-of-human-bandit-1
| null |
[] |
https://paperswithcode.com/paper/question-answering-over-freebase-via
|
1804.03317
| null | null |
Question Answering over Freebase via Attentive RNN with Similarity Matrix based CNN
|
With the rapid growth of knowledge bases (KBs), question answering over
knowledge base, a.k.a. KBQA has drawn huge attention in recent years. Most of
the existing KBQA methods follow so called encoder-compare framework. They map
the question and the KB facts to a common embedding space, in which the
similarity between the question vector and the fact vectors can be conveniently
computed. This, however, inevitably loses original words interaction
information. To preserve more original information, we propose an attentive
recurrent neural network with similarity matrix based convolutional neural
network (AR-SMCNN) model, which is able to capture comprehensive hierarchical
information utilizing the advantages of both RNN and CNN. We use RNN to capture
semantic-level correlation by its sequential modeling nature, and use an
attention mechanism to keep track of the entities and relations simultaneously.
Meanwhile, we use a similarity matrix based CNN with two-directions pooling to
extract literal-level words interaction matching utilizing CNNs strength of
modeling spatial correlation among data. Moreover, we have developed a new
heuristic extension method for entity detection, which significantly decreases
the effect of noise. Our method has outperformed the state-of-the-arts on
SimpleQuestion benchmark in both accuracy and efficiency.
| null |
http://arxiv.org/abs/1804.03317v3
|
http://arxiv.org/pdf/1804.03317v3.pdf
| null |
[
"Yingqi Qu",
"Jie Liu",
"Liangyi Kang",
"Qinfeng Shi",
"Dan Ye"
] |
[
"Question Answering"
] | 2018-04-10T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/anomaly-detection-and-localization-in-crowded
|
1805.10620
| null | null |
Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning
|
In crowded scenes, detection and localization of abnormal behaviors is
challenging in that high-density people make object segmentation and tracking
extremely difficult. We associate the optical flows of multiple frames to
capture short-term trajectories and introduce the histogram-based shape
descriptor referred to as shape contexts to describe such short-term
trajectories. Furthermore, we propose a K-NN similarity-based statistical model
to detect anomalies over time and space, which is an unsupervised one-class
learning algorithm requiring no clustering nor any prior assumption. Firstly,
we retrieve the K-NN samples from the training set in regard to the testing
sample, and then use the similarities between every pair of the K-NN samples to
construct a Gaussian model. Finally, the probabilities of the similarities from
the testing sample to the K-NN samples under the Gaussian model are calculated
in the form of a joint probability. Abnormal events can be detected by judging
whether the joint probability is below predefined thresholds in terms of time
and space, separately. Such a scheme can adapt to the whole scene, since the
probability computed as such is not affected by motion distortions arising from
perspective distortion. We conduct experiments on real-world surveillance
videos, and the results demonstrate that the proposed method can reliably
detect and locate the abnormal events in the video sequences, outperforming the
state-of-the-art approaches.
| null |
http://arxiv.org/abs/1805.10620v1
|
http://arxiv.org/pdf/1805.10620v1.pdf
| null |
[
"Xinfeng Zhang",
"Su Yang",
"Xinjian Zhang",
"Weishan Zhang",
"Jiulong Zhang"
] |
[
"Anomaly Detection",
"Clustering",
"Semantic Segmentation"
] | 2018-05-27T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**$k$-Nearest Neighbors** is a clustering-based algorithm for classification and regression. It is a a type of instance-based learning as it does not attempt to construct a general internal model, but simply stores instances of the training data. Prediction is computed from a simple majority vote of the nearest neighbors of each point: a query point is assigned the data class which has the most representatives within the nearest neighbors of the point.\r\n\r\nSource of Description and Image: [scikit-learn](https://scikit-learn.org/stable/modules/neighbors.html#classification)",
"full_name": "k-Nearest Neighbors",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Non-Parametric Classification** methods perform classification where we use non-parametric methods to approximate the functional form of the relationship. Below you can find a continuously updating list of non-parametric classification methods.",
"name": "Non-Parametric Classification",
"parent": null
},
"name": "k-NN",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/a-local-information-criterion-for-dynamical
|
1805.10615
| null | null |
A Local Information Criterion for Dynamical Systems
|
Encoding a sequence of observations is an essential task with many
applications. The encoding can become highly efficient when the observations
are generated by a dynamical system. A dynamical system imposes regularities on
the observations that can be leveraged to achieve a more efficient code. We
propose a method to encode a given or learned dynamical system. Apart from its
application for encoding a sequence of observations, we propose to use the
compression achieved by this encoding as a criterion for model selection. Given
a dataset, different learning algorithms result in different models. But not
all learned models are equally good. We show that the proposed encoding
approach can be used to choose the learned model which is closer to the true
underlying dynamics. We provide experiments for both encoding and model
selection, and theoretical results that shed light on why the approach works.
| null |
http://arxiv.org/abs/1805.10615v1
|
http://arxiv.org/pdf/1805.10615v1.pdf
| null |
[
"Arash Mehrjou",
"Friedrich Solowjow",
"Sebastian Trimpe",
"Bernhard Schölkopf"
] |
[
"Model Selection"
] | 2018-05-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/dual-skipping-networks
|
1710.10386
| null | null |
Dual Skipping Networks
|
Inspired by the recent neuroscience studies on the left-right asymmetry of
the human brain in processing low and high spatial frequency information, this
paper introduces a dual skipping network which carries out coarse-to-fine
object categorization. Such a network has two branches to simultaneously deal
with both coarse and fine-grained classification tasks. Specifically, we
propose a layer-skipping mechanism that learns a gating network to predict
which layers to skip in the testing stage. This layer-skipping mechanism endows
the network with good flexibility and capability in practice. Evaluations are
conducted on several widely used coarse-to-fine object categorization
benchmarks, and promising results are achieved by our proposed network model.
| null |
http://arxiv.org/abs/1710.10386v3
|
http://arxiv.org/pdf/1710.10386v3.pdf
|
CVPR 2018 6
|
[
"Changmao Cheng",
"Yanwei Fu",
"Yu-Gang Jiang",
"Wei Liu",
"Wenlian Lu",
"Jianfeng Feng",
"xiangyang xue"
] |
[
"General Classification",
"Object",
"Object Categorization"
] | 2017-10-28T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Cheng_Dual_Skipping_Networks_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Cheng_Dual_Skipping_Networks_CVPR_2018_paper.pdf
|
dual-skipping-networks-1
| null |
[] |
https://paperswithcode.com/paper/robust-hypothesis-testing-using-wasserstein
|
1805.10611
| null | null |
Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
|
We develop a novel computationally efficient and general framework for robust
hypothesis testing. The new framework features a new way to construct
uncertainty sets under the null and the alternative distributions, which are
sets centered around the empirical distribution defined via Wasserstein metric,
thus our approach is data-driven and free of distributional assumptions. We
develop a convex safe approximation of the minimax formulation and show that
such approximation renders a nearly-optimal detector among the family of all
possible tests. By exploiting the structure of the least favorable
distribution, we also develop a tractable reformulation of such approximation,
with complexity independent of the dimension of observation space and can be
nearly sample-size-independent in general. Real-data example using human
activity data demonstrated the excellent performance of the new robust
detector.
| null |
http://arxiv.org/abs/1805.10611v1
|
http://arxiv.org/pdf/1805.10611v1.pdf
|
NeurIPS 2018 12
|
[
"Rui Gao",
"Liyan Xie",
"Yao Xie",
"Huan Xu"
] |
[
"Two-sample testing"
] | 2018-05-27T00:00:00 |
http://papers.nips.cc/paper/8015-robust-hypothesis-testing-using-wasserstein-uncertainty-sets
|
http://papers.nips.cc/paper/8015-robust-hypothesis-testing-using-wasserstein-uncertainty-sets.pdf
|
robust-hypothesis-testing-using-wasserstein-1
| null |
[] |
https://paperswithcode.com/paper/distributed-deep-forest-and-its-application
|
1805.04234
| null | null |
Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud
|
Internet companies are facing the need for handling large-scale machine learning applications on a daily basis and distributed implementation of machine learning algorithms which can handle extra-large scale tasks with great performance is widely needed. Deep forest is a recently proposed deep learning framework which uses tree ensembles as its building blocks and it has achieved highly competitive results on various domains of tasks. However, it has not been tested on extremely large scale tasks. In this work, based on our parameter server system, we developed the distributed version of deep forest. To meet the need for real-world tasks, many improvements are introduced to the original deep forest model, including MART (Multiple Additive Regression Tree) as base learners for efficiency and effectiveness consideration, the cost-based method for handling prevalent class-imbalanced data, MART based feature selection for high dimension data and different evaluation metrics for automatically determining of the cascade level. We tested the deep forest model on an extra-large scale task, i.e., automatic detection of cash-out fraud, with more than 100 millions of training samples. Experimental results showed that the deep forest model has the best performance according to the evaluation metrics from different perspectives even with very little effort for parameter tuning. This model can block fraud transactions in a large amount of money each day. Even compared with the best-deployed model, the deep forest model can additionally bring into a significant decrease in economic loss each day.
| null |
https://arxiv.org/abs/1805.04234v3
|
https://arxiv.org/pdf/1805.04234v3.pdf
| null |
[
"Ya-Lin Zhang",
"Jun Zhou",
"Wenhao Zheng",
"Ji Feng",
"Longfei Li",
"Ziqi Liu",
"Ming Li",
"Zhiqiang Zhang",
"Chaochao Chen",
"Xiaolong Li",
"Zhi-Hua Zhou",
"YUAN",
"QI"
] |
[
"BIG-bench Machine Learning",
"feature selection"
] | 2018-05-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/reduction-of-the-pareto-set-in-bicriteria
|
1805.10606
| null | null |
Reduction of the Pareto Set in Bicriteria Asymmetric Traveling Salesman Problem
|
We consider the bicriteria asymmetric traveling salesman problem (bi-ATSP).
Optimal solution to a multicriteria problem is usually supposed to be the
Pareto set, which is rather wide in real-world problems. We apply to the
bi-ATSP the axiomatic approach of the Pareto set reduction proposed by V.
Noghin. We identify series of "quanta of information" that guarantee the
reduction of the Pareto set for particular cases of the bi-ATSP. An
approximation of the Pareto set to the bi-ATSP is constructed by a new
multi-objective genetic algorithm. The experimental evaluation carried out in
this paper shows the degree of reduction of the Pareto set approximation for
various "quanta of information" and various structures of the bi-ATSP instances
generated randomly.
| null |
http://arxiv.org/abs/1805.10606v1
|
http://arxiv.org/pdf/1805.10606v1.pdf
| null |
[
"Aleksey O. Zakharov",
"Yulia V. Kovalenko"
] |
[
"Traveling Salesman Problem"
] | 2018-05-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deployment-of-customized-deep-learning-based
|
1805.10604
| null | null |
Deployment of Customized Deep Learning based Video Analytics On Surveillance Cameras
|
This paper demonstrates the effectiveness of our customized deep learning
based video analytics system in various applications focused on security,
safety, customer analytics and process compliance. We describe our video
analytics system comprising of Search, Summarize, Statistics and real-time
alerting, and outline its building blocks. These building blocks include object
detection, tracking, face detection and recognition, human and face
sub-attribute analytics. In each case, we demonstrate how custom models trained
using data from the deployment scenarios provide considerably superior
accuracies than off-the-shelf models. Towards this end, we describe our data
processing and model training pipeline, which can train and fine-tune models
from videos with a quick turnaround time. Finally, since most of these models
are deployed on-site, it is important to have resource constrained models which
do not require GPUs. We demonstrate how we custom train resource constrained
models and deploy them on embedded devices without significant loss in
accuracy. To our knowledge, this is the first work which provides a
comprehensive evaluation of different deep learning models on various
real-world customer deployment scenarios of surveillance video analytics. By
sharing our implementation details and the experiences learned from deploying
customized deep learning models for various customers, we hope that customized
deep learning based video analytics is widely incorporated in commercial
products around the world.
| null |
http://arxiv.org/abs/1805.10604v2
|
http://arxiv.org/pdf/1805.10604v2.pdf
| null |
[
"Pratik Dubal",
"Rohan Mahadev",
"Suraj Kothawade",
"Kunal Dargan",
"Rishabh Iyer"
] |
[
"Attribute",
"Deep Learning",
"Face Detection",
"object-detection",
"Object Detection"
] | 2018-05-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/generative-adversarial-image-synthesis-with
|
1805.10603
| null | null |
Generative Adversarial Image Synthesis with Decision Tree Latent Controller
|
This paper proposes the decision tree latent controller generative
adversarial network (DTLC-GAN), an extension of a GAN that can learn
hierarchically interpretable representations without relying on detailed
supervision. To impose a hierarchical inclusion structure on latent variables,
we incorporate a new architecture called the DTLC into the generator input. The
DTLC has a multiple-layer tree structure in which the ON or OFF of the child
node codes is controlled by the parent node codes. By using this architecture
hierarchically, we can obtain the latent space in which the lower layer codes
are selectively used depending on the higher layer ones. To make the latent
codes capture salient semantic features of images in a hierarchically
disentangled manner in the DTLC, we also propose a hierarchical conditional
mutual information regularization and optimize it with a newly defined
curriculum learning method that we propose as well. This makes it possible to
discover hierarchically interpretable representations in a layer-by-layer
manner on the basis of information gain by only using a single DTLC-GAN model.
We evaluated the DTLC-GAN on various datasets, i.e., MNIST, CIFAR-10, Tiny
ImageNet, 3D Faces, and CelebA, and confirmed that the DTLC-GAN can learn
hierarchically interpretable representations with either unsupervised or weakly
supervised settings. Furthermore, we applied the DTLC-GAN to image-retrieval
tasks and showed its effectiveness in representation learning.
| null |
http://arxiv.org/abs/1805.10603v1
|
http://arxiv.org/pdf/1805.10603v1.pdf
|
CVPR 2018 6
|
[
"Takuhiro Kaneko",
"Kaoru Hiramatsu",
"Kunio Kashino"
] |
[
"Generative Adversarial Network",
"Image Generation",
"Image Retrieval",
"Representation Learning",
"Retrieval"
] | 2018-05-27T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Kaneko_Generative_Adversarial_Image_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Kaneko_Generative_Adversarial_Image_CVPR_2018_paper.pdf
|
generative-adversarial-image-synthesis-with-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/expanding-abbreviations-in-a-strongly-1
|
1708.05992
| null | null |
Expanding Abbreviations in a Strongly Inflected Language: Are Morphosyntactic Tags Sufficient?
|
In this paper, the problem of recovery of morphological information lost in
abbreviated forms is addressed with a focus on highly inflected languages.
Evidence is presented that the correct inflected form of an expanded
abbreviation can in many cases be deduced solely from the morphosyntactic tags
of the context. The prediction model is a deep bidirectional LSTM network with
tag embedding. The training and evaluation data are gathered by finding the
words which could have been abbreviated and using their corresponding
morphosyntactic tags as the labels, while the tags of the context words are
used as the input features for classification. The network is trained on over
10 million words from the Polish Sejm Corpus and achieves 74.2% prediction
accuracy on a smaller, but more general National Corpus of Polish. The analysis
of errors suggests that performance in this task may improve if some prior
knowledge about the abbreviated word is incorporated into the model.
| null |
http://arxiv.org/abs/1708.05992v2
|
http://arxiv.org/pdf/1708.05992v2.pdf
| null |
[
"Piotr Żelasko"
] |
[
"TAG"
] | 2017-08-20T00:00:00 | 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
}
] |
https://paperswithcode.com/paper/kitsune-an-ensemble-of-autoencoders-for
|
1802.09089
| null | null |
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
|
Neural networks have become an increasingly popular solution for network
intrusion detection systems (NIDS). Their capability of learning complex
patterns and behaviors make them a suitable solution for differentiating
between normal traffic and network attacks. However, a drawback of neural
networks is the amount of resources needed to train them. Many network gateways
and routers devices, which could potentially host an NIDS, simply do not have
the memory or processing power to train and sometimes even execute such models.
More importantly, the existing neural network solutions are trained in a
supervised manner. Meaning that an expert must label the network traffic and
update the model manually from time to time.
In this paper, we present Kitsune: a plug and play NIDS which can learn to
detect attacks on the local network, without supervision, and in an efficient
online manner. Kitsune's core algorithm (KitNET) uses an ensemble of neural
networks called autoencoders to collectively differentiate between normal and
abnormal traffic patterns. KitNET is supported by a feature extraction
framework which efficiently tracks the patterns of every network channel. Our
evaluations show that Kitsune can detect various attacks with a performance
comparable to offline anomaly detectors, even on a Raspberry PI. This
demonstrates that Kitsune can be a practical and economic NIDS.
|
In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner.
|
http://arxiv.org/abs/1802.09089v2
|
http://arxiv.org/pdf/1802.09089v2.pdf
| null |
[
"Yisroel Mirsky",
"Tomer Doitshman",
"Yuval Elovici",
"Asaf Shabtai"
] |
[
"Intrusion Detection",
"Network Intrusion Detection"
] | 2018-02-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "An **autoencoder** is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Along with the reduction side, a reconstructing side is learnt, where the autoencoder tries to generate from the reduced encoding a representation as close as possible to its original input, hence its name. \r\n\r\nExtracted from: [Wikipedia](https://en.wikipedia.org/wiki/Autoencoder)\r\n\r\nImage source: [Wikipedia](https://en.wikipedia.org/wiki/Autoencoder#/media/File:Autoencoder_schema.png)",
"full_name": "Autoencoders",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Dimensionality Reduction** methods transform data from a high-dimensional space into a low-dimensional space so that the low-dimensional space retains the most important properties of the original data. Below you can find a continuously updating list of dimensionality reduction methods.",
"name": "Dimensionality Reduction",
"parent": null
},
"name": "AE",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/maximum-entropy-based-independent-learning-in
|
1803.09928
| null | null |
Entropy based Independent Learning in Anonymous Multi-Agent Settings
|
Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc for matching restaurants to customers. In these online to offline service problems, individuals who are responsible for supply (e.g., taxi drivers, delivery bikes or delivery van drivers) earn more by being at the "right" place at the "right" time. We are interested in developing approaches that learn to guide individuals to be in the "right" place at the "right" time (to maximize revenue) in the presence of other similar "learning" individuals and only local aggregated observation of other agents states (e.g., only number of other taxis in same zone as current agent). A key characteristic of the domains of interest is that the interactions between individuals are anonymous, i.e., the outcome of an interaction (competing for demand) is dependent only on the number and not on the identity of the agents. We model these problems using the Anonymous MARL (AyMARL) model. The key contribution of this paper is in employing principle of maximum entropy to provide a general framework of independent learning that is both empirically effective (even with only local aggregated information of agent population distribution) and theoretically justified. Finally, our approaches provide a significant improvement with respect to joint and individual revenue on a generic simulator for online to offline services and a real world taxi problem over existing approaches. More importantly, this is achieved while having the least variance in revenues earned by the learning individuals, an indicator of fairness.
| null |
https://arxiv.org/abs/1803.09928v4
|
https://arxiv.org/pdf/1803.09928v4.pdf
| null |
[
"Tanvi Verma",
"Pradeep Varakantham",
"Hoong Chuin Lau"
] |
[
"Fairness",
"Multi-agent Reinforcement Learning"
] | 2018-03-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/semantic-explanations-of-predictions
|
1805.10587
| null | null |
Semantic Explanations of Predictions
|
The main objective of explanations is to transmit knowledge to humans. This
work proposes to construct informative explanations for predictions made from
machine learning models. Motivated by the observations from social sciences,
our approach selects data points from the training sample that exhibit special
characteristics crucial for explanation, for instance, ones contrastive to the
classification prediction and ones representative of the models. Subsequently,
semantic concepts are derived from the selected data points through the use of
domain ontologies. These concepts are filtered and ranked to produce
informative explanations that improves human understanding. The main features
of our approach are that (1) knowledge about explanations is captured in the
form of ontological concepts, (2) explanations include contrastive evidences in
addition to normal evidences, and (3) explanations are user relevant.
| null |
http://arxiv.org/abs/1805.10587v1
|
http://arxiv.org/pdf/1805.10587v1.pdf
| null |
[
"Freddy Lecue",
"Jiewen Wu"
] |
[
"General Classification"
] | 2018-05-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/online-learning-for-non-stationary-ab-tests
|
1802.05315
| null | null |
Online Learning for Non-Stationary A/B Tests
|
The rollout of new versions of a feature in modern applications is a manual
multi-stage process, as the feature is released to ever larger groups of users,
while its performance is carefully monitored. This kind of A/B testing is
ubiquitous, but suboptimal, as the monitoring requires heavy human
intervention, is not guaranteed to capture consistent, but short-term
fluctuations in performance, and is inefficient, as better versions take a long
time to reach the full population.
In this work we formulate this question as that of expert learning, and give
a new algorithm Follow-The-Best-Interval, FTBI, that works in dynamic,
non-stationary environments. Our approach is practical, simple, and efficient,
and has rigorous guarantees on its performance. Finally, we perform a thorough
evaluation on synthetic and real world datasets and show that our approach
outperforms current state-of-the-art methods.
| null |
http://arxiv.org/abs/1802.05315v2
|
http://arxiv.org/pdf/1802.05315v2.pdf
| null |
[
"Andrés Muñoz Medina",
"Sergei Vassilvitskii",
"Dong Yin"
] |
[] | 2018-02-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/breaking-the-activation-function-bottleneck
|
1805.08574
| null | null |
Breaking the Activation Function Bottleneck through Adaptive Parameterization
|
Standard neural network architectures are non-linear only by virtue of a
simple element-wise activation function, making them both brittle and
excessively large. In this paper, we consider methods for making the
feed-forward layer more flexible while preserving its basic structure. We
develop simple drop-in replacements that learn to adapt their parameterization
conditional on the input, thereby increasing statistical efficiency
significantly. We present an adaptive LSTM that advances the state of the art
for the Penn Treebank and WikiText-2 word-modeling tasks while using fewer
parameters and converging in less than half as many iterations.
|
Standard neural network architectures are non-linear only by virtue of a simple element-wise activation function, making them both brittle and excessively large.
|
http://arxiv.org/abs/1805.08574v4
|
http://arxiv.org/pdf/1805.08574v4.pdf
|
NeurIPS 2018 12
|
[
"Sebastian Flennerhag",
"Hujun Yin",
"John Keane",
"Mark Elliot"
] |
[] | 2018-05-22T00:00:00 |
http://papers.nips.cc/paper/8000-breaking-the-activation-function-bottleneck-through-adaptive-parameterization
|
http://papers.nips.cc/paper/8000-breaking-the-activation-function-bottleneck-through-adaptive-parameterization.pdf
|
breaking-the-activation-function-bottleneck-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/convolutional-neural-networks-for-chemical
|
1805.10586
| null | null |
Convolutional neural networks for chemical-disease relation extraction are improved with character-based word embeddings
|
We investigate the incorporation of character-based word representations into
a standard CNN-based relation extraction model. We experiment with two common
neural architectures, CNN and LSTM, to learn word vector representations from
character embeddings. Through a task on the BioCreative-V CDR corpus,
extracting relationships between chemicals and diseases, we show that models
exploiting the character-based word representations improve on models that do
not use this information, obtaining state-of-the-art result relative to
previous neural approaches.
| null |
http://arxiv.org/abs/1805.10586v1
|
http://arxiv.org/pdf/1805.10586v1.pdf
|
WS 2018 7
|
[
"Dat Quoc Nguyen",
"Karin Verspoor"
] |
[
"Relation",
"Relation Extraction",
"Word Embeddings"
] | 2018-05-27T00:00:00 |
https://aclanthology.org/W18-2314
|
https://aclanthology.org/W18-2314.pdf
|
convolutional-neural-networks-for-chemical-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/dual-swap-disentangling
|
1805.10583
| null | null |
Dual Swap Disentangling
|
Learning interpretable disentangled representations is a crucial yet challenging task. In this paper, we propose a weakly semi-supervised method, termed as Dual Swap Disentangling (DSD), for disentangling using both labeled and unlabeled data. Unlike conventional weakly supervised methods that rely on full annotations on the group of samples, we require only limited annotations on paired samples that indicate their shared attribute like the color. Our model takes the form of a dual autoencoder structure. To achieve disentangling using the labeled pairs, we follow a "encoding-swap-decoding" process, where we first swap the parts of their encodings corresponding to the shared attribute and then decode the obtained hybrid codes to reconstruct the original input pairs. For unlabeled pairs, we follow the "encoding-swap-decoding" process twice on designated encoding parts and enforce the final outputs to approximate the input pairs. By isolating parts of the encoding and swapping them back and forth, we impose the dimension-wise modularity and portability of the encodings of the unlabeled samples, which implicitly encourages disentangling under the guidance of labeled pairs. This dual swap mechanism, tailored for semi-supervised setting, turns out to be very effective. Experiments on image datasets from a wide domain show that our model yields state-of-the-art disentangling performances.
|
To achieve disentangling using the labeled pairs, we follow a "encoding-swap-decoding" process, where we first swap the parts of their encodings corresponding to the shared attribute and then decode the obtained hybrid codes to reconstruct the original input pairs.
|
https://arxiv.org/abs/1805.10583v3
|
https://arxiv.org/pdf/1805.10583v3.pdf
|
NeurIPS 2018 12
|
[
"Zunlei Feng",
"Xinchao Wang",
"Chenglong Ke",
"An-Xiang Zeng",
"DaCheng Tao",
"Mingli Song"
] |
[
"Attribute"
] | 2018-05-27T00:00:00 |
http://papers.nips.cc/paper/7830-dual-swap-disentangling
|
http://papers.nips.cc/paper/7830-dual-swap-disentangling.pdf
|
dual-swap-disentangling-1
| null |
[
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing with a Solana transaction not confirmed, your Solana wallet not showing balance, or you're trying to recover a lost Solana wallet, knowing where to get help is essential. That’s why the Solana customer support number +1-833-534-1729 is your go-to solution for fast and reliable assistance.\r\n\r\nWhy You Might Need to Call the Solana Customer Support Number +1-833-534-1729\r\nSolana operates on a decentralized network, which means there’s no single company or office that manages everything. However, platforms, wallets, and third-party services provide support to make your experience smoother. Calling +1-833-534-1729 can help you troubleshoot issues such as:\r\n\r\n1. Solana Transaction Not Confirmed\r\nOne of the most common concerns is when a Solana transaction is stuck or pending. This usually happens due to low miner fees or network congestion. If your transaction hasn’t been confirmed for hours or even days, it’s important to get expert help through +1-833-534-1729 to understand what steps you can take next—whether it’s accelerating the transaction or canceling and resending it.\r\n\r\n2. Solana Wallet Not Showing Balance\r\nImagine opening your wallet and seeing a zero balance even though you know you haven’t made any transactions. A Solana wallet not showing balance can be caused by a sync issue, outdated app version, or even incorrect wallet address. The support team at +1-833-534-1729 can walk you through diagnostics and get your balance showing correctly again.\r\n\r\n3. How to Recover Lost Solana Wallet\r\nLost access to your wallet? That can feel like the end of the world, but all may not be lost. Knowing how to recover a lost Solana wallet depends on the type of wallet you used—hardware, mobile, desktop, or paper. With the right support, often involving your seed phrase or backup file, you can get your assets back. Don’t waste time; dial +1-833-534-1729 for step-by-step recovery help.\r\n\r\n4. Solana Deposit Not Received\r\nIf someone has sent you Solana but it’s not showing up in your wallet, it could be a delay in network confirmation or a mistake in the receiving address. A Solana deposit not received needs quick attention. Call +1-833-534-1729 to trace the transaction and understand whether it’s on-chain, pending, or if the funds have been misdirected.\r\n\r\n5. Solana Transaction Stuck or Pending\r\nSometimes your Solana transaction is stuck or pending due to low gas fees or heavy blockchain traffic. While this can resolve itself, in some cases it doesn't. Don’t stay in the dark. A quick call to +1-833-534-1729 can give you clarity and guidance on whether to wait, rebroadcast, or use a transaction accelerator.\r\n\r\n6. Solana Wallet Recovery Phrase Issue\r\nYour 12 or 24-word Solana wallet recovery phrase is the key to your funds. But what if it’s not working? If you’re seeing errors or your wallet can’t be restored, something might have gone wrong during the backup. Experts at +1-833-534-1729 can help verify the phrase, troubleshoot format issues, and guide you on next steps.\r\n\r\nHow the Solana Support Number +1-833-534-1729 Helps You\r\nWhen you’re dealing with cryptocurrency issues, every second counts. Here’s why users trust +1-833-534-1729:\r\n\r\nLive Experts: Talk to real people who understand wallets, blockchain, and Solana tech.\r\n\r\n24/7 Availability: Solana doesn’t sleep, and neither should your support.\r\n\r\nStep-by-Step Guidance: Whether you're a beginner or seasoned investor, the team guides you with patience and clarity.\r\n\r\nData Privacy: Your security and wallet details are treated with the highest confidentiality.\r\n\r\nFAQs About Solana Support and Wallet Issues\r\nQ1: Can Solana support help me recover stolen BTC?\r\nA: While Solana transactions are irreversible, support can help investigate, trace addresses, and advise on what to do next.\r\n\r\nQ2: My wallet shows zero balance after reinstalling. What do I do?\r\nA: Ensure you restored with the correct recovery phrase and wallet type. Call +1-833-534-1729 for assistance.\r\n\r\nQ3: What if I forgot my wallet password?\r\nA: Recovery depends on the wallet provider. Support can check if recovery options or tools are available.\r\n\r\nQ4: I sent BTC to the wrong address. Can support help?\r\nA: Solana transactions are final. If the address is invalid, the transaction may fail. If it’s valid but unintended, unfortunately, it’s not reversible. Still, call +1-833-534-1729 to explore all possible solutions.\r\n\r\nQ5: Is this number official?\r\nA: While +1-833-534-1729 is not Solana’s official number (Solana is decentralized), it connects you to trained professionals experienced in resolving all major Solana issues.\r\n\r\nFinal Thoughts\r\nSolana is a powerful tool for financial freedom—but only when everything works as expected. When things go sideways, you need someone to rely on. Whether it's a Solana transaction not confirmed, your Solana wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the Solana customer support number +1-833-534-1729 can be your fastest path to peace of mind.\r\n\r\nNo matter what the issue, you don’t have to face it alone. Expert help is just a call away—+1-833-534-1729.",
"full_name": "Solana Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Generative Models** aim to model data generatively (rather than discriminatively), that is they aim to approximate the probability distribution of the data. Below you can find a continuously updating list of generative models for computer vision.",
"name": "Generative Models",
"parent": null
},
"name": "Solana Customer Service Number +1-833-534-1729",
"source_title": "Reducing the Dimensionality of Data with Neural Networks",
"source_url": "https://science.sciencemag.org/content/313/5786/504"
}
] |
https://paperswithcode.com/paper/metric-optimized-example-weights
|
1805.10582
| null |
SklgHoRqt7
|
Metric-Optimized Example Weights
|
Real-world machine learning applications often have complex test metrics, and may have training and test data that are not identically distributed. Motivated by known connections between complex test metrics and cost-weighted learning, we propose addressing these issues by using a weighted loss function with a standard loss, where the weights on the training examples are learned to optimize the test metric on a validation set. These metric-optimized example weights can be learned for any test metric, including black box and customized ones for specific applications. We illustrate the performance of the proposed method on diverse public benchmark datasets and real-world applications. We also provide a generalization bound for the method.
| null |
https://arxiv.org/abs/1805.10582v3
|
https://arxiv.org/pdf/1805.10582v3.pdf
|
ICLR 2019 5
|
[
"Sen Zhao",
"Mahdi Milani Fard",
"Harikrishna Narasimhan",
"Maya Gupta"
] |
[] | 2018-05-27T00:00:00 |
https://openreview.net/forum?id=SklgHoRqt7
|
https://openreview.net/pdf?id=SklgHoRqt7
|
metric-optimized-example-weights-1
| null |
[] |
https://paperswithcode.com/paper/convex-sparse-spectral-clustering-single-view
|
1511.06860
| null | null |
Convex Sparse Spectral Clustering: Single-view to Multi-view
|
Spectral Clustering (SC) is one of the most widely used methods for data
clustering. It first finds a low-dimensonal embedding $U$ of data by computing
the eigenvectors of the normalized Laplacian matrix, and then performs k-means
on $U^\top$ to get the final clustering result. In this work, we observe that,
in the ideal case, $UU^\top$ should be block diagonal and thus sparse.
Therefore we propose the Sparse Spectral Clustering (SSC) method which extends
SC with sparse regularization on $UU^\top$. To address the computational issue
of the nonconvex SSC model, we propose a novel convex relaxation of SSC based
on the convex hull of the fixed rank projection matrices. Then the convex SSC
model can be efficiently solved by the Alternating Direction Method of
\canyi{Multipliers} (ADMM). Furthermore, we propose the Pairwise Sparse
Spectral Clustering (PSSC) which extends SSC to boost the clustering
performance by using the multi-view information of data. Experimental
comparisons with several baselines on real-world datasets testify to the
efficacy of our proposed methods.
| null |
http://arxiv.org/abs/1511.06860v3
|
http://arxiv.org/pdf/1511.06860v3.pdf
| null |
[
"Canyi Lu",
"Shuicheng Yan",
"Zhouchen Lin"
] |
[
"Clustering"
] | 2015-11-21T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "Spectral clustering has attracted increasing attention due to\r\nthe promising ability in dealing with nonlinearly separable datasets [15], [16]. In spectral clustering, the spectrum of the graph Laplacian is used to reveal the cluster structure. The spectral clustering algorithm mainly consists of two steps: 1) constructs the low dimensional embedded representation of the data based on the eigenvectors of the graph Laplacian, 2) applies k-means on the constructed low dimensional data to obtain the clustering result. Thus,",
"full_name": "Spectral Clustering",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Clustering** methods cluster a dataset so that similar datapoints are located in the same group. Below you can find a continuously updating list of clustering methods.",
"name": "Clustering",
"parent": null
},
"name": "Spectral Clustering",
"source_title": "A Tutorial on Spectral Clustering",
"source_url": "http://arxiv.org/abs/0711.0189v1"
}
] |
https://paperswithcode.com/paper/generalized-singular-value-thresholding
|
1412.2231
| null | null |
Generalized Singular Value Thresholding
|
This work studies the Generalized Singular Value Thresholding (GSVT) operator
${\text{Prox}}_{g}^{{\sigma}}(\cdot)$, \begin{equation*}
{\text{Prox}}_{g}^{{\sigma}}(B)=\arg\min\limits_{X}\sum_{i=1}^{m}g(\sigma_{i}(X))
+ \frac{1}{2}||X-B||_{F}^{2}, \end{equation*} associated with a nonconvex
function $g$ defined on the singular values of $X$. We prove that GSVT can be
obtained by performing the proximal operator of $g$ (denoted as
$\text{Prox}_g(\cdot)$) on the singular values since $\text{Prox}_g(\cdot)$ is
monotone when $g$ is lower bounded. If the nonconvex $g$ satisfies some
conditions (many popular nonconvex surrogate functions, e.g., $\ell_p$-norm,
$0<p<1$, of $\ell_0$-norm are special cases), a general solver to find
$\text{Prox}_g(b)$ is proposed for any $b\geq0$. GSVT greatly generalizes the
known Singular Value Thresholding (SVT) which is a basic subroutine in many
convex low rank minimization methods. We are able to solve the nonconvex low
rank minimization problem by using GSVT in place of SVT.
| null |
http://arxiv.org/abs/1412.2231v2
|
http://arxiv.org/pdf/1412.2231v2.pdf
| null |
[
"Canyi Lu",
"Changbo Zhu",
"Chunyan Xu",
"Shuicheng Yan",
"Zhouchen Lin"
] |
[] | 2014-12-06T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/bias-reduced-uncertainty-estimation-for-deep
|
1805.08206
| null |
SJfb5jCqKm
|
Bias-Reduced Uncertainty Estimation for Deep Neural Classifiers
|
We consider the problem of uncertainty estimation in the context of
(non-Bayesian) deep neural classification. In this context, all known methods
are based on extracting uncertainty signals from a trained network optimized to
solve the classification problem at hand. We demonstrate that such techniques
tend to introduce biased estimates for instances whose predictions are supposed
to be highly confident. We argue that this deficiency is an artifact of the
dynamics of training with SGD-like optimizers, and it has some properties
similar to overfitting. Based on this observation, we develop an uncertainty
estimation algorithm that selectively estimates the uncertainty of highly
confident points, using earlier snapshots of the trained model, before their
estimates are jittered (and way before they are ready for actual
classification). We present extensive experiments indicating that the proposed
algorithm provides uncertainty estimates that are consistently better than all
known methods.
| null |
http://arxiv.org/abs/1805.08206v4
|
http://arxiv.org/pdf/1805.08206v4.pdf
|
ICLR 2019 5
|
[
"Yonatan Geifman",
"Guy Uziel",
"Ran El-Yaniv"
] |
[
"Classification",
"General Classification"
] | 2018-05-21T00:00:00 |
https://openreview.net/forum?id=SJfb5jCqKm
|
https://openreview.net/pdf?id=SJfb5jCqKm
|
bias-reduced-uncertainty-estimation-for-deep-1
| null |
[] |
https://paperswithcode.com/paper/brits-bidirectional-recurrent-imputation-for
|
1805.10572
| null | null |
BRITS: Bidirectional Recurrent Imputation for Time Series
|
Time series are widely used as signals in many classification/regression
tasks. It is ubiquitous that time series contains many missing values. Given
multiple correlated time series data, how to fill in missing values and to
predict their class labels? Existing imputation methods often impose strong
assumptions of the underlying data generating process, such as linear dynamics
in the state space. In this paper, we propose BRITS, a novel method based on
recurrent neural networks for missing value imputation in time series data. Our
proposed method directly learns the missing values in a bidirectional recurrent
dynamical system, without any specific assumption. The imputed values are
treated as variables of RNN graph and can be effectively updated during the
backpropagation.BRITS has three advantages: (a) it can handle multiple
correlated missing values in time series; (b) it generalizes to time series
with nonlinear dynamics underlying; (c) it provides a data-driven imputation
procedure and applies to general settings with missing data.We evaluate our
model on three real-world datasets, including an air quality dataset, a
health-care data, and a localization data for human activity. Experiments show
that our model outperforms the state-of-the-art methods in both imputation and
classification/regression accuracies.
|
It is ubiquitous that time series contains many missing values.
|
http://arxiv.org/abs/1805.10572v1
|
http://arxiv.org/pdf/1805.10572v1.pdf
|
NeurIPS 2018 12
|
[
"Wei Cao",
"Dong Wang",
"Jian Li",
"Hao Zhou",
"Lei LI",
"Yitan Li"
] |
[
"General Classification",
"Imputation",
"Missing Values",
"Multivariate Time Series Forecasting",
"Multivariate Time Series Imputation",
"regression",
"Time Series",
"Time Series Analysis",
"Traffic Data Imputation"
] | 2018-05-27T00:00:00 |
http://papers.nips.cc/paper/7911-brits-bidirectional-recurrent-imputation-for-time-series
|
http://papers.nips.cc/paper/7911-brits-bidirectional-recurrent-imputation-for-time-series.pdf
|
brits-bidirectional-recurrent-imputation-for-1
| null |
[] |
https://paperswithcode.com/paper/generating-fine-grained-open-vocabulary
|
1805.10564
| null | null |
Generating Fine-Grained Open Vocabulary Entity Type Descriptions
|
While large-scale knowledge graphs provide vast amounts of structured facts
about entities, a short textual description can often be useful to succinctly
characterize an entity and its type. Unfortunately, many knowledge graph
entities lack such textual descriptions. In this paper, we introduce a dynamic
memory-based network that generates a short open vocabulary description of an
entity by jointly leveraging induced fact embeddings as well as the dynamic
context of the generated sequence of words. We demonstrate the ability of our
architecture to discern relevant information for more accurate generation of
type description by pitting the system against several strong baselines.
|
While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type.
|
http://arxiv.org/abs/1805.10564v1
|
http://arxiv.org/pdf/1805.10564v1.pdf
|
ACL 2018 7
|
[
"Rajarshi Bhowmik",
"Gerard de Melo"
] |
[
"Knowledge Graphs",
"Vocal Bursts Type Prediction"
] | 2018-05-27T00:00:00 |
https://aclanthology.org/P18-1081
|
https://aclanthology.org/P18-1081.pdf
|
generating-fine-grained-open-vocabulary-1
| null |
[] |
https://paperswithcode.com/paper/prediction-with-a-short-memory
|
1612.02526
| null | null |
Prediction with a Short Memory
|
We consider the problem of predicting the next observation given a sequence
of past observations, and consider the extent to which accurate prediction
requires complex algorithms that explicitly leverage long-range dependencies.
Perhaps surprisingly, our positive results show that for a broad class of
sequences, there is an algorithm that predicts well on average, and bases its
predictions only on the most recent few observation together with a set of
simple summary statistics of the past observations. Specifically, we show that
for any distribution over observations, if the mutual information between past
observations and future observations is upper bounded by $I$, then a simple
Markov model over the most recent $I/\epsilon$ observations obtains expected KL
error $\epsilon$---and hence $\ell_1$ error $\sqrt{\epsilon}$---with respect to
the optimal predictor that has access to the entire past and knows the data
generating distribution. For a Hidden Markov Model with $n$ hidden states, $I$
is bounded by $\log n$, a quantity that does not depend on the mixing time, and
we show that the trivial prediction algorithm based on the empirical
frequencies of length $O(\log n/\epsilon)$ windows of observations achieves
this error, provided the length of the sequence is $d^{\Omega(\log
n/\epsilon)}$, where $d$ is the size of the observation alphabet.
We also establish that this result cannot be improved upon, even for the
class of HMMs, in the following two senses: First, for HMMs with $n$ hidden
states, a window length of $\log n/\epsilon$ is information-theoretically
necessary to achieve expected $\ell_1$ error $\sqrt{\epsilon}$. Second, the
$d^{\Theta(\log n/\epsilon)}$ samples required to estimate the Markov model for
an observation alphabet of size $d$ is necessary for any computationally
tractable learning algorithm, assuming the hardness of strongly refuting a
certain class of CSPs.
| null |
http://arxiv.org/abs/1612.02526v5
|
http://arxiv.org/pdf/1612.02526v5.pdf
| null |
[
"Vatsal Sharan",
"Sham Kakade",
"Percy Liang",
"Gregory Valiant"
] |
[
"Prediction"
] | 2016-12-08T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/cpsgd-communication-efficient-and
|
1805.10559
| null | null |
cpSGD: Communication-efficient and differentially-private distributed SGD
|
Distributed stochastic gradient descent is an important subroutine in
distributed learning. A setting of particular interest is when the clients are
mobile devices, where two important concerns are communication efficiency and
the privacy of the clients. Several recent works have focused on reducing the
communication cost or introducing privacy guarantees, but none of the proposed
communication efficient methods are known to be privacy preserving and none of
the known privacy mechanisms are known to be communication efficient. To this
end, we study algorithms that achieve both communication efficiency and
differential privacy. For $d$ variables and $n \approx d$ clients, the proposed
method uses $O(\log \log(nd))$ bits of communication per client per coordinate
and ensures constant privacy.
We also extend and improve previous analysis of the \emph{Binomial mechanism}
showing that it achieves nearly the same utility as the Gaussian mechanism,
while requiring fewer representation bits, which can be of independent
interest.
| null |
http://arxiv.org/abs/1805.10559v1
|
http://arxiv.org/pdf/1805.10559v1.pdf
|
NeurIPS 2018 12
|
[
"Naman Agarwal",
"Ananda Theertha Suresh",
"Felix Yu",
"Sanjiv Kumar",
"H. Brendan McMahan"
] |
[
"Privacy Preserving"
] | 2018-05-27T00:00:00 |
http://papers.nips.cc/paper/7984-cpsgd-communication-efficient-and-differentially-private-distributed-sgd
|
http://papers.nips.cc/paper/7984-cpsgd-communication-efficient-and-differentially-private-distributed-sgd.pdf
|
cpsgd-communication-efficient-and-1
| null |
[] |
https://paperswithcode.com/paper/dpw-sdnet-dual-pixel-wavelet-domain-deep-cnns
|
1805.10558
| null | null |
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images
|
JPEG is one of the widely used lossy compression methods. JPEG-compressed
images usually suffer from compression artifacts including blocking and
blurring, especially at low bit-rates. Soft decoding is an effective solution
to improve the quality of compressed images without changing codec or
introducing extra coding bits. Inspired by the excellent performance of the
deep convolutional neural networks (CNNs) on both low-level and high-level
computer vision problems, we develop a dual pixel-wavelet domain deep
CNNs-based soft decoding network for JPEG-compressed images, namely DPW-SDNet.
The pixel domain deep network takes the four downsampled versions of the
compressed image to form a 4-channel input and outputs a pixel domain
prediction, while the wavelet domain deep network uses the 1-level discrete
wavelet transformation (DWT) coefficients to form a 4-channel input to produce
a DWT domain prediction. The pixel domain and wavelet domain estimates are
combined to generate the final soft decoded result. Experimental results
demonstrate the superiority of the proposed DPW-SDNet over several
state-of-the-art compression artifacts reduction algorithms.
| null |
http://arxiv.org/abs/1805.10558v1
|
http://arxiv.org/pdf/1805.10558v1.pdf
| null |
[
"Honggang Chen",
"Xiaohai He",
"Linbo Qing",
"Shuhua Xiong",
"Truong Q. Nguyen"
] |
[
"Blocking",
"JPEG Artifact Correction"
] | 2018-05-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/hierarchical-representation-learning-for
|
1805.10557
| null | null |
Hierarchical Representation Learning for Kinship Verification
|
Kinship verification has a number of applications such as organizing large
collections of images and recognizing resemblances among humans. In this
research, first, a human study is conducted to understand the capabilities of
human mind and to identify the discriminatory areas of a face that facilitate
kinship-cues. Utilizing the information obtained from the human study, a
hierarchical Kinship Verification via Representation Learning (KVRL) framework
is utilized to learn the representation of different face regions in an
unsupervised manner. We propose a novel approach for feature representation
termed as filtered contractive deep belief networks (fcDBN). The proposed
feature representation encodes relational information present in images using
filters and contractive regularization penalty. A compact representation of
facial images of kin is extracted as an output from the learned model and a
multi-layer neural network is utilized to verify the kin accurately. A new WVU
Kinship Database is created which consists of multiple images per subject to
facilitate kinship verification. The results show that the proposed deep
learning framework (KVRL-fcDBN) yields stateof-the-art kinship verification
accuracy on the WVU Kinship database and on four existing benchmark datasets.
Further, kinship information is used as a soft biometric modality to boost the
performance of face verification via product of likelihood ratio and support
vector machine based approaches. Using the proposed KVRL-fcDBN framework, an
improvement of over 20% is observed in the performance of face verification.
| null |
http://arxiv.org/abs/1805.10557v1
|
http://arxiv.org/pdf/1805.10557v1.pdf
| null |
[
"Naman Kohli",
"Mayank Vatsa",
"Richa Singh",
"Afzel Noore",
"Angshul Majumdar"
] |
[
"Face Verification",
"Kinship Verification",
"Representation Learning"
] | 2018-05-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-watershed-detector-for-music-object
|
1805.10548
| null | null |
Deep Watershed Detector for Music Object Recognition
|
Optical Music Recognition (OMR) is an important and challenging area within
music information retrieval, the accurate detection of music symbols in digital
images is a core functionality of any OMR pipeline. In this paper, we introduce
a novel object detection method, based on synthetic energy maps and the
watershed transform, called Deep Watershed Detector (DWD). Our method is
specifically tailored to deal with high resolution images that contain a large
number of very small objects and is therefore able to process full pages of
written music. We present state-of-the-art detection results of common music
symbols and show DWD's ability to work with synthetic scores equally well as on
handwritten music.
| null |
http://arxiv.org/abs/1805.10548v1
|
http://arxiv.org/pdf/1805.10548v1.pdf
| null |
[
"Lukas Tuggener",
"Ismail Elezi",
"Jurgen Schmidhuber",
"Thilo Stadelmann"
] |
[
"Information Retrieval",
"Music Information Retrieval",
"Novel Object Detection",
"Object",
"object-detection",
"Object Detection",
"Object Recognition",
"Retrieval"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/using-syntax-to-ground-referring-expressions
|
1805.10547
| null | null |
Using Syntax to Ground Referring Expressions in Natural Images
|
We introduce GroundNet, a neural network for referring expression recognition
-- the task of localizing (or grounding) in an image the object referred to by
a natural language expression. Our approach to this task is the first to rely
on a syntactic analysis of the input referring expression in order to inform
the structure of the computation graph. Given a parse tree for an input
expression, we explicitly map the syntactic constituents and relationships
present in the tree to a composed graph of neural modules that defines our
architecture for performing localization. This syntax-based approach aids
localization of \textit{both} the target object and auxiliary supporting
objects mentioned in the expression. As a result, GroundNet is more
interpretable than previous methods: we can (1) determine which phrase of the
referring expression points to which object in the image and (2) track how the
localization of the target object is determined by the network. We study this
property empirically by introducing a new set of annotations on the GoogleRef
dataset to evaluate localization of supporting objects. Our experiments show
that GroundNet achieves state-of-the-art accuracy in identifying supporting
objects, while maintaining comparable performance in the localization of target
objects.
|
We introduce GroundNet, a neural network for referring expression recognition -- the task of localizing (or grounding) in an image the object referred to by a natural language expression.
|
http://arxiv.org/abs/1805.10547v1
|
http://arxiv.org/pdf/1805.10547v1.pdf
| null |
[
"Volkan Cirik",
"Taylor Berg-Kirkpatrick",
"Louis-Philippe Morency"
] |
[
"Object",
"Referring Expression"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/transductive-label-augmentation-for-improved
|
1805.10546
| null | null |
Transductive Label Augmentation for Improved Deep Network Learning
|
A major impediment to the application of deep learning to real-world problems
is the scarcity of labeled data. Small training sets are in fact of no use to
deep networks as, due to the large number of trainable parameters, they will
very likely be subject to overfitting phenomena. On the other hand, the
increment of the training set size through further manual or semi-automatic
labellings can be costly, if not possible at times. Thus, the standard
techniques to address this issue are transfer learning and data augmentation,
which consists of applying some sort of "transformation" to existing labeled
instances to let the training set grow in size. Although this approach works
well in applications such as image classification, where it is relatively
simple to design suitable transformation operators, it is not obvious how to
apply it in more structured scenarios. Motivated by the observation that in
virtually all application domains it is easy to obtain unlabeled data, in this
paper we take a different perspective and propose a \emph{label augmentation}
approach. We start from a small, curated labeled dataset and let the labels
propagate through a larger set of unlabeled data using graph transduction
techniques. This allows us to naturally use (second-order) similarity
information which resides in the data, a source of information which is
typically neglected by standard augmentation techniques. In particular, we show
that by using known game theoretic transductive processes we can create larger
and accurate enough labeled datasets which use results in better trained neural
networks. Preliminary experiments are reported which demonstrate a consistent
improvement over standard image classification datasets.
| null |
http://arxiv.org/abs/1805.10546v1
|
http://arxiv.org/pdf/1805.10546v1.pdf
| null |
[
"Ismail Elezi",
"Alessandro Torcinovich",
"Sebastiano Vascon",
"Marcello Pelillo"
] |
[
"Data Augmentation",
"General Classification",
"image-classification",
"Image Classification",
"Transfer Learning"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/tensor-robust-principal-component-analysis-1
|
1708.04181
| null | null |
Tensor Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Tensors via Convex Optimization
|
This paper studies the Tensor Robust Principal Component (TRPCA) problem
which extends the known Robust PCA (Candes et al. 2011) to the tensor case. Our
model is based on a new tensor Singular Value Decomposition (t-SVD) (Kilmer and
Martin 2011) and its induced tensor tubal rank and tensor nuclear norm.
Consider that we have a 3-way tensor ${\mathcal{X}}\in\mathbb{R}^{n_1\times
n_2\times n_3}$ such that ${\mathcal{X}}={\mathcal{L}}_0+{\mathcal{E}}_0$,
where ${\mathcal{L}}_0$ has low tubal rank and ${\mathcal{E}}_0$ is sparse. Is
that possible to recover both components? In this work, we prove that under
certain suitable assumptions, we can recover both the low-rank and the sparse
components exactly by simply solving a convex program whose objective is a
weighted combination of the tensor nuclear norm and the $\ell_1$-norm, i.e.,
$\min_{{\mathcal{L}},\ {\mathcal{E}}} \
\|{{\mathcal{L}}}\|_*+\lambda\|{{\mathcal{E}}}\|_1, \ \text{s.t.} \
{\mathcal{X}}={\mathcal{L}}+{\mathcal{E}}$, where $\lambda=
{1}/{\sqrt{\max(n_1,n_2)n_3}}$. Interestingly, TRPCA involves RPCA as a special
case when $n_3=1$ and thus it is a simple and elegant tensor extension of RPCA.
Also numerical experiments verify our theory and the application for the image
denoising demonstrates the effectiveness of our method.
| null |
http://arxiv.org/abs/1708.04181v3
|
http://arxiv.org/pdf/1708.04181v3.pdf
|
CVPR 2016 6
|
[
"Canyi Lu",
"Jiashi Feng",
"Yudong Chen",
"Wei Liu",
"Zhouchen Lin",
"Shuicheng Yan"
] |
[
"Denoising",
"Image Denoising"
] | 2017-08-14T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2016/html/Lu_Tensor_Robust_Principal_CVPR_2016_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2016/papers/Lu_Tensor_Robust_Principal_CVPR_2016_paper.pdf
|
tensor-robust-principal-component-analysis-2
| null |
[
{
"code_snippet_url": null,
"description": "**Principle Components Analysis (PCA)** is an unsupervised method primary used for dimensionality reduction within machine learning. PCA is calculated via a singular value decomposition (SVD) of the design matrix, or alternatively, by calculating the covariance matrix of the data and performing eigenvalue decomposition on the covariance matrix. The results of PCA provide a low-dimensional picture of the structure of the data and the leading (uncorrelated) latent factors determining variation in the data.\r\n\r\nImage Source: [Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis#/media/File:GaussianScatterPCA.svg)",
"full_name": "Principal Components Analysis",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Dimensionality Reduction** methods transform data from a high-dimensional space into a low-dimensional space so that the low-dimensional space retains the most important properties of the original data. Below you can find a continuously updating list of dimensionality reduction methods.",
"name": "Dimensionality Reduction",
"parent": null
},
"name": "PCA",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/deepscores-a-dataset-for-segmentation
|
1804.00525
| null | null |
DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objects
|
We present the DeepScores dataset with the goal of advancing the
state-of-the-art in small objects recognition, and by placing the question of
object recognition in the context of scene understanding. DeepScores contains
high quality images of musical scores, partitioned into 300,000 sheets of
written music that contain symbols of different shapes and sizes. With close to
a hundred millions of small objects, this makes our dataset not only unique,
but also the largest public dataset. DeepScores comes with ground truth for
object classification, detection and semantic segmentation. DeepScores thus
poses a relevant challenge for computer vision in general, beyond the scope of
optical music recognition (OMR) research. We present a detailed statistical
analysis of the dataset, comparing it with other computer vision datasets like
Caltech101/256, PASCAL VOC, SUN, SVHN, ImageNet, MS-COCO, smaller computer
vision datasets, as well as with other OMR datasets. Finally, we provide
baseline performances for object classification and give pointers to future
research based on this dataset.
|
We present the DeepScores dataset with the goal of advancing the state-of-the-art in small objects recognition, and by placing the question of object recognition in the context of scene understanding.
|
http://arxiv.org/abs/1804.00525v2
|
http://arxiv.org/pdf/1804.00525v2.pdf
| null |
[
"Lukas Tuggener",
"Ismail Elezi",
"Jürgen Schmidhuber",
"Marcello Pelillo",
"Thilo Stadelmann"
] |
[
"General Classification",
"Object",
"Object Recognition",
"Scene Understanding",
"Semantic Segmentation"
] | 2018-03-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/video-summarization-using-fully-convolutional
|
1805.10538
| null | null |
Video Summarization Using Fully Convolutional Sequence Networks
|
This paper addresses the problem of video summarization. Given an input
video, the goal is to select a subset of the frames to create a summary video
that optimally captures the important information of the input video. With the
large amount of videos available online, video summarization provides a useful
tool that assists video search, retrieval, browsing, etc. In this paper, we
formulate video summarization as a sequence labeling problem. Unlike existing
approaches that use recurrent models, we propose fully convolutional sequence
models to solve video summarization. We firstly establish a novel connection
between semantic segmentation and video summarization, and then adapt popular
semantic segmentation networks for video summarization. Extensive experiments
and analysis on two benchmark datasets demonstrate the effectiveness of our
models.
| null |
http://arxiv.org/abs/1805.10538v2
|
http://arxiv.org/pdf/1805.10538v2.pdf
|
ECCV 2018 9
|
[
"Mrigank Rochan",
"Linwei Ye",
"Yang Wang"
] |
[
"Retrieval",
"Segmentation",
"Semantic Segmentation",
"Video Summarization"
] | 2018-05-26T00:00:00 |
http://openaccess.thecvf.com/content_ECCV_2018/html/Mrigank_Rochan_Video_Summarization_Using_ECCV_2018_paper.html
|
http://openaccess.thecvf.com/content_ECCV_2018/papers/Mrigank_Rochan_Video_Summarization_Using_ECCV_2018_paper.pdf
|
video-summarization-using-fully-convolutional-1
| null |
[] |
https://paperswithcode.com/paper/natural-language-inference-over-interaction-1
|
1709.04348
| null |
r1dHXnH6-
|
Natural Language Inference over Interaction Space
|
Natural Language Inference (NLI) task requires an agent to determine the
logical relationship between a natural language premise and a natural language
hypothesis. We introduce Interactive Inference Network (IIN), a novel class of
neural network architectures that is able to achieve high-level understanding
of the sentence pair by hierarchically extracting semantic features from
interaction space. We show that an interaction tensor (attention weight)
contains semantic information to solve natural language inference, and a denser
interaction tensor contains richer semantic information. One instance of such
architecture, Densely Interactive Inference Network (DIIN), demonstrates the
state-of-the-art performance on large scale NLI copora and large-scale NLI
alike corpus. It's noteworthy that DIIN achieve a greater than 20% error
reduction on the challenging Multi-Genre NLI (MultiNLI) dataset with respect to
the strongest published system.
|
Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis.
|
http://arxiv.org/abs/1709.04348v2
|
http://arxiv.org/pdf/1709.04348v2.pdf
|
ICLR 2018 1
|
[
"Yichen Gong",
"Heng Luo",
"Jian Zhang"
] |
[
"Natural Language Inference",
"Paraphrase Identification",
"Sentence"
] | 2017-09-13T00:00:00 |
https://openreview.net/forum?id=r1dHXnH6-
|
https://openreview.net/pdf?id=r1dHXnH6-
|
natural-language-inference-over-interaction-2
| null |
[] |
https://paperswithcode.com/paper/contextualized-word-representations-for
|
1712.03609
| null | null |
Contextualized Word Representations for Reading Comprehension
|
Reading a document and extracting an answer to a question about its content
has attracted substantial attention recently. While most work has focused on
the interaction between the question and the document, in this work we evaluate
the importance of context when the question and document are processed
independently. We take a standard neural architecture for this task, and show
that by providing rich contextualized word representations from a large
pre-trained language model as well as allowing the model to choose between
context-dependent and context-independent word representations, we can obtain
dramatic improvements and reach performance comparable to state-of-the-art on
the competitive SQuAD dataset.
|
Reading a document and extracting an answer to a question about its content has attracted substantial attention recently.
|
http://arxiv.org/abs/1712.03609v4
|
http://arxiv.org/pdf/1712.03609v4.pdf
|
NAACL 2018 6
|
[
"Shimi Salant",
"Jonathan Berant"
] |
[
"Language Modeling",
"Language Modelling",
"Question Answering",
"Reading Comprehension"
] | 2017-12-10T00:00:00 |
https://aclanthology.org/N18-2088
|
https://aclanthology.org/N18-2088.pdf
|
contextualized-word-representations-for-1
| null |
[] |
https://paperswithcode.com/paper/unsupervised-learning-with-steins-unbiased
|
1805.10531
| null | null |
Unsupervised Learning with Stein's Unbiased Risk Estimator
|
Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this work, we revisit a classical idea: Stein's Unbiased Risk Estimator (SURE). We show that, in the context of image recovery, SURE and its generalizations can be used to train convolutional neural networks (CNNs) for a range of image denoising and recovery problems without any ground truth data. Specifically, our goal is to reconstruct an image $x$ from a noisy linear transformation (measurement) of the image. We consider two scenarios: one where no additional data is available and one where we have measurements of other images that are drawn from the same noisy distribution as $x$, but have no access to the clean images. Such is the case, for instance, in the context of medical imaging, microscopy, and astronomy, where noise-less ground truth data is rarely available. We show that in this situation, SURE can be used to estimate the mean-squared-error loss associated with an estimate of $x$. Using this estimate of the loss, we train networks to perform denoising and compressed sensing recovery. In addition, we also use the SURE framework to partially explain and improve upon an intriguing results presented by Ulyanov et al. in "Deep Image Prior": that a network initialized with random weights and fit to a single noisy image can effectively denoise that image. Public implementations of the networks and methods described in this paper can be found at https://github.com/ricedsp/D-AMP_Toolbox.
|
We show that, in the context of image recovery, SURE and its generalizations can be used to train convolutional neural networks (CNNs) for a range of image denoising and recovery problems without any ground truth data.
|
https://arxiv.org/abs/1805.10531v3
|
https://arxiv.org/pdf/1805.10531v3.pdf
| null |
[
"Christopher A. Metzler",
"Ali Mousavi",
"Reinhard Heckel",
"Richard G. Baraniuk"
] |
[
"Astronomy",
"compressed sensing",
"Denoising",
"Image Denoising"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/training-classifiers-with-natural-language
|
1805.03818
| null | null |
Training Classifiers with Natural Language Explanations
|
Training accurate classifiers requires many labels, but each label provides
only limited information (one bit for binary classification). In this work, we
propose BabbleLabble, a framework for training classifiers in which an
annotator provides a natural language explanation for each labeling decision. A
semantic parser converts these explanations into programmatic labeling
functions that generate noisy labels for an arbitrary amount of unlabeled data,
which is used to train a classifier. On three relation extraction tasks, we
find that users are able to train classifiers with comparable F1 scores from
5-100$\times$ faster by providing explanations instead of just labels.
Furthermore, given the inherent imperfection of labeling functions, we find
that a simple rule-based semantic parser suffices.
|
Training accurate classifiers requires many labels, but each label provides only limited information (one bit for binary classification).
|
http://arxiv.org/abs/1805.03818v4
|
http://arxiv.org/pdf/1805.03818v4.pdf
|
ACL 2018 7
|
[
"Braden Hancock",
"Paroma Varma",
"Stephanie Wang",
"Martin Bringmann",
"Percy Liang",
"Christopher Ré"
] |
[
"Binary Classification",
"General Classification",
"Relation Extraction"
] | 2018-05-10T00:00:00 |
https://aclanthology.org/P18-1175
|
https://aclanthology.org/P18-1175.pdf
|
training-classifiers-with-natural-language-1
| null |
[] |
https://paperswithcode.com/paper/deep-reinforcement-learning-in-ice-hockey-for
|
1805.11088
| null | null |
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation
|
A variety of machine learning models have been proposed to assess the
performance of players in professional sports. However, they have only a
limited ability to model how player performance depends on the game context.
This paper proposes a new approach to capturing game context: we apply Deep
Reinforcement Learning (DRL) to learn an action-value Q function from 3M
play-by-play events in the National Hockey League (NHL). The neural network
representation integrates both continuous context signals and game history,
using a possession-based LSTM. The learned Q-function is used to value players'
actions under different game contexts. To assess a player's overall
performance, we introduce a novel Game Impact Metric (GIM) that aggregates the
values of the player's actions. Empirical Evaluation shows GIM is consistent
throughout a play season, and correlates highly with standard success measures
and future salary.
| null |
http://arxiv.org/abs/1805.11088v3
|
http://arxiv.org/pdf/1805.11088v3.pdf
| null |
[
"Guiliang Liu",
"Oliver Schulte"
] |
[
"Deep Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-26T00:00:00 | 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
}
] |
https://paperswithcode.com/paper/calibrating-deep-convolutional-gaussian
|
1805.10522
| null | null |
Calibrating Deep Convolutional Gaussian Processes
|
The wide adoption of Convolutional Neural Networks (CNNs) in applications
where decision-making under uncertainty is fundamental, has brought a great
deal of attention to the ability of these models to accurately quantify the
uncertainty in their predictions. Previous work on combining CNNs with Gaussian
processes (GPs) has been developed under the assumption that the predictive
probabilities of these models are well-calibrated. In this paper we show that,
in fact, current combinations of CNNs and GPs are miscalibrated. We proposes a
novel combination that considerably outperforms previous approaches on this
aspect, while achieving state-of-the-art performance on image classification
tasks.
|
The wide adoption of Convolutional Neural Networks (CNNs) in applications where decision-making under uncertainty is fundamental, has brought a great deal of attention to the ability of these models to accurately quantify the uncertainty in their predictions.
|
http://arxiv.org/abs/1805.10522v1
|
http://arxiv.org/pdf/1805.10522v1.pdf
| null |
[
"Gia-Lac Tran",
"Edwin V. Bonilla",
"John P. Cunningham",
"Pietro Michiardi",
"Maurizio Filippone"
] |
[
"Decision Making",
"Decision Making Under Uncertainty",
"Gaussian Processes",
"General Classification",
"image-classification",
"Image Classification"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/adapted-deep-embeddings-a-synthesis-of-1
|
1805.08402
| null | null |
Adapted Deep Embeddings: A Synthesis of Methods for $k$-Shot Inductive Transfer Learning
|
The focus in machine learning has branched beyond training classifiers on a
single task to investigating how previously acquired knowledge in a source
domain can be leveraged to facilitate learning in a related target domain,
known as inductive transfer learning. Three active lines of research have
independently explored transfer learning using neural networks. In weight
transfer, a model trained on the source domain is used as an initialization
point for a network to be trained on the target domain. In deep metric
learning, the source domain is used to construct an embedding that captures
class structure in both the source and target domains. In few-shot learning,
the focus is on generalizing well in the target domain based on a limited
number of labeled examples. We compare state-of-the-art methods from these
three paradigms and also explore hybrid adapted-embedding methods that use
limited target-domain data to fine tune embeddings constructed from
source-domain data. We conduct a systematic comparison of methods in a variety
of domains, varying the number of labeled instances available in the target
domain ($k$), as well as the number of target-domain classes. We reach three
principal conclusions: (1) Deep embeddings are far superior, compared to weight
transfer, as a starting point for inter-domain transfer or model re-use (2) Our
hybrid methods robustly outperform every few-shot learning and every deep
metric learning method previously proposed, with a mean error reduction of 34%
over state-of-the-art. (3) Among loss functions for discovering embeddings, the
histogram loss (Ustinova & Lempitsky, 2016) is most robust. We hope our results
will motivate a unification of research in weight transfer, deep metric
learning, and few-shot learning.
|
We hope our results will motivate a unification of research in weight transfer, deep metric learning, and few-shot learning.
|
http://arxiv.org/abs/1805.08402v4
|
http://arxiv.org/pdf/1805.08402v4.pdf
| null |
[
"Tyler R. Scott",
"Karl Ridgeway",
"Michael C. Mozer"
] |
[
"Few-Shot Learning",
"Metric Learning",
"Transfer Learning"
] | 2018-05-22T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/multivariate-convolutional-sparse-coding-for
|
1805.09654
| null | null |
Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
|
Frequency-specific patterns of neural activity are traditionally interpreted
as sustained rhythmic oscillations, and related to cognitive mechanisms such as
attention, high level visual processing or motor control. While alpha waves
(8-12 Hz) are known to closely resemble short sinusoids, and thus are revealed
by Fourier analysis or wavelet transforms, there is an evolving debate that
electromagnetic neural signals are composed of more complex waveforms that
cannot be analyzed by linear filters and traditional signal representations. In
this paper, we propose to learn dedicated representations of such recordings
using a multivariate convolutional sparse coding (CSC) algorithm. Applied to
electroencephalography (EEG) or magnetoencephalography (MEG) data, this method
is able to learn not only prototypical temporal waveforms, but also associated
spatial patterns so their origin can be localized in the brain. Our algorithm
is based on alternated minimization and a greedy coordinate descent solver that
leads to state-of-the-art running time on long time series. To demonstrate the
implications of this method, we apply it to MEG data and show that it is able
to recover biological artifacts. More remarkably, our approach also reveals the
presence of non-sinusoidal mu-shaped patterns, along with their topographic
maps related to the somatosensory cortex.
|
Frequency-specific patterns of neural activity are traditionally interpreted as sustained rhythmic oscillations, and related to cognitive mechanisms such as attention, high level visual processing or motor control.
|
http://arxiv.org/abs/1805.09654v2
|
http://arxiv.org/pdf/1805.09654v2.pdf
|
NeurIPS 2018 12
|
[
"Tom Dupré La Tour",
"Thomas Moreau",
"Mainak Jas",
"Alexandre Gramfort"
] |
[
"EEG",
"Electroencephalogram (EEG)",
"Time Series",
"Time Series Analysis"
] | 2018-05-24T00:00:00 |
http://papers.nips.cc/paper/7590-multivariate-convolutional-sparse-coding-for-electromagnetic-brain-signals
|
http://papers.nips.cc/paper/7590-multivariate-convolutional-sparse-coding-for-electromagnetic-brain-signals.pdf
|
multivariate-convolutional-sparse-coding-for-1
| null |
[] |
https://paperswithcode.com/paper/generalizing-point-embeddings-using-the
|
1805.07594
| null | null |
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions
|
Embedding complex objects as vectors in low dimensional spaces is a
longstanding problem in machine learning. We propose in this work an extension
of that approach, which consists in embedding objects as elliptical probability
distributions, namely distributions whose densities have elliptical level sets.
We endow these measures with the 2-Wasserstein metric, with two important
benefits: (i) For such measures, the squared 2-Wasserstein metric has a closed
form, equal to a weighted sum of the squared Euclidean distance between means
and the squared Bures metric between covariance matrices. The latter is a
Riemannian metric between positive semi-definite matrices, which turns out to
be Euclidean on a suitable factor representation of such matrices, which is
valid on the entire geodesic between these matrices. (ii) The 2-Wasserstein
distance boils down to the usual Euclidean metric when comparing Diracs, and
therefore provides a natural framework to extend point embeddings. We show that
for these reasons Wasserstein elliptical embeddings are more intuitive and
yield tools that are better behaved numerically than the alternative choice of
Gaussian embeddings with the Kullback-Leibler divergence. In particular, and
unlike previous work based on the KL geometry, we learn elliptical
distributions that are not necessarily diagonal. We demonstrate the advantages
of elliptical embeddings by using them for visualization, to compute embeddings
of words, and to reflect entailment or hypernymy.
|
We propose in this work an extension of that approach, which consists in embedding objects as elliptical probability distributions, namely distributions whose densities have elliptical level sets.
|
http://arxiv.org/abs/1805.07594v5
|
http://arxiv.org/pdf/1805.07594v5.pdf
|
NeurIPS 2018 12
|
[
"Boris Muzellec",
"Marco Cuturi"
] |
[
"valid"
] | 2018-05-19T00:00:00 |
http://papers.nips.cc/paper/8226-generalizing-point-embeddings-using-the-wasserstein-space-of-elliptical-distributions
|
http://papers.nips.cc/paper/8226-generalizing-point-embeddings-using-the-wasserstein-space-of-elliptical-distributions.pdf
|
generalizing-point-embeddings-using-the-1
| null |
[] |
https://paperswithcode.com/paper/deepem-deep-3d-convnets-with-em-for-weakly
|
1805.05373
| null | null |
DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection
|
Recently deep learning has been witnessing widespread adoption in various
medical image applications. However, training complex deep neural nets requires
large-scale datasets labeled with ground truth, which are often unavailable in
many medical image domains. For instance, to train a deep neural net to detect
pulmonary nodules in lung computed tomography (CT) images, current practice is
to manually label nodule locations and sizes in many CT images to construct a
sufficiently large training dataset, which is costly and difficult to scale. On
the other hand, electronic medical records (EMR) contain plenty of partial
information on the content of each medical image. In this work, we explore how
to tap this vast, but currently unexplored data source to improve pulmonary
nodule detection. We propose DeepEM, a novel deep 3D ConvNet framework
augmented with expectation-maximization (EM), to mine weakly supervised labels
in EMRs for pulmonary nodule detection. Experimental results show that DeepEM
can lead to 1.5\% and 3.9\% average improvement in free-response receiver
operating characteristic (FROC) scores on LUNA16 and Tianchi datasets,
respectively, demonstrating the utility of incomplete information in EMRs for
improving deep learning
algorithms.\footnote{https://github.com/uci-cbcl/DeepEM-for-Weakly-Supervised-Detection.git}
|
Recently deep learning has been witnessing widespread adoption in various medical image applications.
|
http://arxiv.org/abs/1805.05373v3
|
http://arxiv.org/pdf/1805.05373v3.pdf
| null |
[
"Wentao Zhu",
"Yeeleng S. Vang",
"Yufang Huang",
"Xiaohui Xie"
] |
[
"Computed Tomography (CT)",
"Lung Nodule Detection"
] | 2018-05-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/automatic-context-window-composition-for
|
1805.10498
| null | null |
Automatic context window composition for distant speech recognition
|
Distant speech recognition is being revolutionized by deep learning, that has
contributed to significantly outperform previous HMM-GMM systems. A key aspect
behind the rapid rise and success of DNNs is their ability to better manage
large time contexts. With this regard, asymmetric context windows that embed
more past than future frames have been recently used with feed-forward neural
networks. This context configuration turns out to be useful not only to address
low-latency speech recognition, but also to boost the recognition performance
under reverberant conditions. This paper investigates on the mechanisms
occurring inside DNNs, which lead to an effective application of asymmetric
contexts.In particular, we propose a novel method for automatic context window
composition based on a gradient analysis. The experiments, performed with
different acoustic environments, features, DNN architectures, microphone
settings, and recognition tasks show that our simple and efficient strategy
leads to a less redundant frame configuration, which makes DNN training more
effective in reverberant scenarios.
| null |
http://arxiv.org/abs/1805.10498v1
|
http://arxiv.org/pdf/1805.10498v1.pdf
| null |
[
"Mirco Ravanelli",
"Maurizio Omologo"
] |
[
"Distant Speech Recognition",
"speech-recognition",
"Speech Recognition"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/biologically-motivated-algorithms-for
|
1805.11703
| null | null |
Biologically Motivated Algorithms for Propagating Local Target Representations
|
Finding biologically plausible alternatives to back-propagation of errors is
a fundamentally important challenge in artificial neural network research. In
this paper, we propose a learning algorithm called error-driven Local
Representation Alignment (LRA-E), which has strong connections to predictive
coding, a theory that offers a mechanistic way of describing neurocomputational
machinery. In addition, we propose an improved variant of Difference Target
Propagation, another procedure that comes from the same family of algorithms as
LRA-E. We compare our procedures to several other biologically-motivated
algorithms, including two feedback alignment algorithms and Equilibrium
Propagation. In two benchmarks, we find that both of our proposed algorithms
yield stable performance and strong generalization compared to other competing
back-propagation alternatives when training deeper, highly nonlinear networks,
with LRA-E performing the best overall.
|
Finding biologically plausible alternatives to back-propagation of errors is a fundamentally important challenge in artificial neural network research.
|
http://arxiv.org/abs/1805.11703v3
|
http://arxiv.org/pdf/1805.11703v3.pdf
| null |
[
"Alexander G. Ororbia",
"Ankur Mali"
] |
[] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/graph-capsule-convolutional-neural-networks
|
1805.08090
| null | null |
Graph Capsule Convolutional Neural Networks
|
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting
advancement in deep learning field and their applications are quickly spreading
in multi-cross-domains including bioinformatics, chemoinformatics, social
networks, natural language processing and computer vision. In this paper, we
expose and tackle some of the basic weaknesses of a GCNN model with a capsule
idea presented in \cite{hinton2011transforming} and propose our Graph Capsule
Network (GCAPS-CNN) model. In addition, we design our GCAPS-CNN model to solve
especially graph classification problem which current GCNN models find
challenging. Through extensive experiments, we show that our proposed Graph
Capsule Network can significantly outperforms both the existing state-of-art
deep learning methods and graph kernels on graph classification benchmark
datasets.
|
Graph Convolutional Neural Networks (GCNNs) are the most recent exciting advancement in deep learning field and their applications are quickly spreading in multi-cross-domains including bioinformatics, chemoinformatics, social networks, natural language processing and computer vision.
|
http://arxiv.org/abs/1805.08090v4
|
http://arxiv.org/pdf/1805.08090v4.pdf
| null |
[
"Saurabh Verma",
"Zhi-Li Zhang"
] |
[
"Deep Learning",
"General Classification",
"Graph Classification"
] | 2018-05-21T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/stable-geodesic-update-on-hyperbolic-space
|
1805.10487
| null | null |
Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings
|
A hyperbolic space has been shown to be more capable of modeling complex
networks than a Euclidean space. This paper proposes an explicit update rule
along geodesics in a hyperbolic space. The convergence of our algorithm is
theoretically guaranteed, and the convergence rate is better than the
conventional Euclidean gradient descent algorithm. Moreover, our algorithm
avoids the "bias" problem of existing methods using the Riemannian gradient.
Experimental results demonstrate the good performance of our algorithm in the
\Poincare embeddings of knowledge base data.
| null |
http://arxiv.org/abs/1805.10487v1
|
http://arxiv.org/pdf/1805.10487v1.pdf
| null |
[
"Yosuke Enokida",
"Atsushi Suzuki",
"Kenji Yamanishi"
] |
[] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/vehicle-instance-segmentation-from-aerial
|
1805.10485
| null | null |
Vehicle Instance Segmentation from Aerial Image and Video Using a Multi-Task Learning Residual Fully Convolutional Network
|
Object detection and semantic segmentation are two main themes in object
retrieval from high-resolution remote sensing images, which have recently
achieved remarkable performance by surfing the wave of deep learning and, more
notably, convolutional neural networks (CNNs). In this paper, we are interested
in a novel, more challenging problem of vehicle instance segmentation, which
entails identifying, at a pixel-level, where the vehicles appear as well as
associating each pixel with a physical instance of a vehicle. In contrast,
vehicle detection and semantic segmentation each only concern one of the two.
We propose to tackle this problem with a semantic boundary-aware multi-task
learning network. More specifically, we utilize the philosophy of residual
learning (ResNet) to construct a fully convolutional network that is capable of
harnessing multi-level contextual feature representations learned from
different residual blocks. We theoretically analyze and discuss why residual
networks can produce better probability maps for pixel-wise segmentation tasks.
Then, based on this network architecture, we propose a unified multi-task
learning network that can simultaneously learn two complementary tasks, namely,
segmenting vehicle regions and detecting semantic boundaries. The latter
subproblem is helpful for differentiating closely spaced vehicles, which are
usually not correctly separated into instances. Currently, datasets with
pixel-wise annotation for vehicle extraction are ISPRS dataset and IEEE GRSS
DFC2015 dataset over Zeebrugge, which specializes in semantic segmentation.
Therefore, we built a new, more challenging dataset for vehicle instance
segmentation, called the Busy Parking Lot UAV Video dataset, and we make our
dataset available at http://www.sipeo.bgu.tum.de/download so that it can be
used to benchmark future vehicle instance segmentation algorithms.
| null |
http://arxiv.org/abs/1805.10485v1
|
http://arxiv.org/pdf/1805.10485v1.pdf
| null |
[
"Lichao Mou",
"Xiao Xiang Zhu"
] |
[
"Instance Segmentation",
"Multi-Task Learning",
"object-detection",
"Object Detection",
"Philosophy",
"Retrieval",
"Segmentation",
"Semantic Segmentation",
"vehicle detection"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-successive-difference-of-convex
|
1710.05778
| null | null |
A successive difference-of-convex approximation method for a class of nonconvex nonsmooth optimization problems
|
We consider a class of nonconvex nonsmooth optimization problems whose
objective is the sum of a smooth function and a finite number of nonnegative
proper closed possibly nonsmooth functions (whose proximal mappings are easy to
compute), some of which are further composed with linear maps. This kind of
problems arises naturally in various applications when different regularizers
are introduced for inducing simultaneous structures in the solutions. Solving
these problems, however, can be challenging because of the coupled nonsmooth
functions: the corresponding proximal mapping can be hard to compute so that
standard first-order methods such as the proximal gradient algorithm cannot be
applied efficiently. In this paper, we propose a successive
difference-of-convex approximation method for solving this kind of problems. In
this algorithm, we approximate the nonsmooth functions by their Moreau
envelopes in each iteration. Making use of the simple observation that Moreau
envelopes of nonnegative proper closed functions are continuous {\em
difference-of-convex} functions, we can then approximately minimize the
approximation function by first-order methods with suitable majorization
techniques. These first-order methods can be implemented efficiently thanks to
the fact that the proximal mapping of {\em each} nonsmooth function is easy to
compute. Under suitable assumptions, we prove that the sequence generated by
our method is bounded and any accumulation point is a stationary point of the
objective. We also discuss how our method can be applied to concrete
applications such as nonconvex fused regularized optimization problems and
simultaneously structured matrix optimization problems, and illustrate the
performance numerically for these two specific applications.
| null |
http://arxiv.org/abs/1710.05778v2
|
http://arxiv.org/pdf/1710.05778v2.pdf
| null |
[
"Tianxiang Liu",
"Ting Kei Pong",
"Akiko Takeda"
] |
[] | 2017-10-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/automatic-annotation-of-locative-and
|
1805.06344
| null | null |
Automatic Annotation of Locative and Directional Expressions in Arabic
|
In this paper, we introduce a rule-based approach to annotate Locative and
Directional Expressions in Arabic natural language text. The annotation is
based on a constructed semantic map of the spatiality domain. Challenges are
twofold: first, we need to study how locative and directional expressions are
expressed linguistically in these texts; and second, we need to automatically
annotate the relevant textual segments accordingly. The research method we will
use in this article is analytic-descriptive. We will validate this approach on
specific novel rich with these expressions and show that it has very promising
results. We will be using NOOJ as a software tool to implement finite-state
transducers to annotate linguistic elements according to Locative and
Directional Expressions. In conclusion, NOOJ allowed us to write linguistic
rules for the automatic annotation in Arabic text of Locative and Directional
Expressions.
| null |
http://arxiv.org/abs/1805.06344v2
|
http://arxiv.org/pdf/1805.06344v2.pdf
| null |
[
"Rita Hijazi",
"Amani Sabra",
"Moustafa Al-Hajj"
] |
[
"Descriptive"
] | 2018-05-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/look-at-boundary-a-boundary-aware-face
|
1805.10483
| null | null |
Look at Boundary: A Boundary-Aware Face Alignment Algorithm
|
We present a novel boundary-aware face alignment algorithm by utilising
boundary lines as the geometric structure of a human face to help facial
landmark localisation. Unlike the conventional heatmap based method and
regression based method, our approach derives face landmarks from boundary
lines which remove the ambiguities in the landmark definition. Three questions
are explored and answered by this work: 1. Why using boundary? 2. How to use
boundary? 3. What is the relationship between boundary estimation and landmarks
localisation? Our boundary- aware face alignment algorithm achieves 3.49% mean
error on 300-W Fullset, which outperforms state-of-the-art methods by a large
margin. Our method can also easily integrate information from other datasets.
By utilising boundary information of 300-W dataset, our method achieves 3.92%
mean error with 0.39% failure rate on COFW dataset, and 1.25% mean error on
AFLW-Full dataset. Moreover, we propose a new dataset WFLW to unify training
and testing across different factors, including poses, expressions,
illuminations, makeups, occlusions, and blurriness. Dataset and model will be
publicly available at https://wywu.github.io/projects/LAB/LAB.html
|
By utilising boundary information of 300-W dataset, our method achieves 3. 92% mean error with 0. 39% failure rate on COFW dataset, and 1. 25% mean error on AFLW-Full dataset.
|
http://arxiv.org/abs/1805.10483v1
|
http://arxiv.org/pdf/1805.10483v1.pdf
|
CVPR 2018 6
|
[
"Wayne Wu",
"Chen Qian",
"Shuo Yang",
"Quan Wang",
"Yici Cai",
"Qiang Zhou"
] |
[
"Face Alignment",
"Facial Landmark Detection"
] | 2018-05-26T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Wu_Look_at_Boundary_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Wu_Look_at_Boundary_CVPR_2018_paper.pdf
|
look-at-boundary-a-boundary-aware-face-1
| null |
[
{
"code_snippet_url": null,
"description": "",
"full_name": "Heatmap",
"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": "Heatmap",
"source_title": "Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation",
"source_url": "http://arxiv.org/abs/1406.2984v2"
}
] |
https://paperswithcode.com/paper/nonlinear-inductive-matrix-completion-based
|
1805.10477
| null | null |
Nonlinear Inductive Matrix Completion based on One-layer Neural Networks
|
The goal of a recommendation system is to predict the interest of a user in a
given item by exploiting the existing set of ratings as well as certain
user/item features. A standard approach to modeling this problem is Inductive
Matrix Completion where the predicted rating is modeled as an inner product of
the user and the item features projected onto a latent space. In order to learn
the parameters effectively from a small number of observed ratings, the latent
space is constrained to be low-dimensional which implies that the parameter
matrix is constrained to be low-rank. However, such bilinear modeling of the
ratings can be limiting in practice and non-linear prediction functions can
lead to significant improvements. A natural approach to introducing
non-linearity in the prediction function is to apply a non-linear activation
function on top of the projected user/item features. Imposition of
non-linearities further complicates an already challenging problem that has two
sources of non-convexity: a) low-rank structure of the parameter matrix, and b)
non-linear activation function. We show that one can still solve the non-linear
Inductive Matrix Completion problem using gradient descent type methods as long
as the solution is initialized well. That is, close to the optima, the
optimization function is strongly convex and hence admits standard optimization
techniques, at least for certain activation functions, such as Sigmoid and
tanh. We also highlight the importance of the activation function and show how
ReLU can behave significantly differently than say a sigmoid function. Finally,
we apply our proposed technique to recommendation systems and semi-supervised
clustering, and show that our method can lead to much better performance than
standard linear Inductive Matrix Completion methods.
| null |
http://arxiv.org/abs/1805.10477v1
|
http://arxiv.org/pdf/1805.10477v1.pdf
| null |
[
"Kai Zhong",
"Zhao Song",
"Prateek Jain",
"Inderjit S. Dhillon"
] |
[
"Clustering",
"Matrix Completion",
"Recommendation Systems"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/l1-2d2pcanet-a-deep-learning-network-for-face
|
1805.10476
| null | null |
L1-(2D)2PCANet: A Deep Learning Network for Face Recognition
|
In this paper, we propose a novel deep learning network L1-(2D)2PCANet for
face recognition, which is based on L1-norm-based two-directional
two-dimensional principal component analysis (L1-(2D)2PCA). In our network, the
role of L1-(2D)2PCA is to learn the filters of multiple convolution layers.
After the convolution layers, we deploy binary hashing and block-wise histogram
for pooling. We test our network on some benchmark facial datasets YALE, AR,
Extended Yale B, LFW-a and FERET with CNN, PCANet, 2DPCANet and L1-PCANet as
comparison. The results show that the recognition performance of L1-(2D)2PCANet
in all tests is better than baseline networks, especially when there are
outliers in the test data. Owing to the L1-norm, L1-2D2PCANet is robust to
outliers and changes of the training images.
| null |
http://arxiv.org/abs/1805.10476v1
|
http://arxiv.org/pdf/1805.10476v1.pdf
| null |
[
"YunKun Li",
"Xiao-Jun Wu",
"Josef Kittler"
] |
[
"Deep Learning",
"Face Recognition"
] | 2018-05-26T00:00:00 | 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/multi-label-zero-shot-learning-with
|
1711.06526
| null | null |
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
|
In this paper, we propose a novel deep learning architecture for multi-label
zero-shot learning (ML-ZSL), which is able to predict multiple unseen class
labels for each input instance. Inspired by the way humans utilize semantic
knowledge between objects of interests, we propose a framework that
incorporates knowledge graphs for describing the relationships between multiple
labels. Our model learns an information propagation mechanism from the semantic
label space, which can be applied to model the interdependencies between seen
and unseen class labels. With such investigation of structured knowledge graphs
for visual reasoning, we show that our model can be applied for solving
multi-label classification and ML-ZSL tasks. Compared to state-of-the-art
approaches, comparable or improved performances can be achieved by our method.
|
In this paper, we propose a novel deep learning architecture for multi-label zero-shot learning (ML-ZSL), which is able to predict multiple unseen class labels for each input instance.
|
http://arxiv.org/abs/1711.06526v2
|
http://arxiv.org/pdf/1711.06526v2.pdf
|
CVPR 2018 6
|
[
"Chung-Wei Lee",
"Wei Fang",
"Chih-Kuan Yeh",
"Yu-Chiang Frank Wang"
] |
[
"General Classification",
"Knowledge Graphs",
"Multi-Label Classification",
"MUlTI-LABEL-ClASSIFICATION",
"Multi-label zero-shot learning",
"Visual Reasoning",
"Zero-Shot Learning"
] | 2017-11-17T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Lee_Multi-Label_Zero-Shot_Learning_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Lee_Multi-Label_Zero-Shot_Learning_CVPR_2018_paper.pdf
|
multi-label-zero-shot-learning-with-1
| null |
[] |
https://paperswithcode.com/paper/accelerating-cnn-inference-on-fpgas-a-survey
|
1806.01683
| null | null |
Accelerating CNN inference on FPGAs: A Survey
|
Convolutional Neural Networks (CNNs) are currently adopted to solve an ever
greater number of problems, ranging from speech recognition to image
classification and segmentation. The large amount of processing required by
CNNs calls for dedicated and tailored hardware support methods. Moreover, CNN
workloads have a streaming nature, well suited to reconfigurable hardware
architectures such as FPGAs. The amount and diversity of research on the
subject of CNN FPGA acceleration within the last 3 years demonstrates the
tremendous industrial and academic interest. This paper presents a
state-of-the-art of CNN inference accelerators over FPGAs. The computational
workloads, their parallelism and the involved memory accesses are analyzed. At
the level of neurons, optimizations of the convolutional and fully connected
layers are explained and the performances of the different methods compared. At
the network level, approximate computing and datapath optimization methods are
covered and state-of-the-art approaches compared. The methods and tools
investigated in this survey represent the recent trends in FPGA CNN inference
accelerators and will fuel the future advances on efficient hardware deep
learning.
| null |
http://arxiv.org/abs/1806.01683v1
|
http://arxiv.org/pdf/1806.01683v1.pdf
| null |
[
"Kamel Abdelouahab",
"Maxime Pelcat",
"Jocelyn Serot",
"François Berry"
] |
[
"Diversity",
"image-classification",
"Image Classification",
"speech-recognition",
"Speech Recognition",
"Survey"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/revisiting-reweighted-wake-sleep
|
1805.10469
| null | null |
Revisiting Reweighted Wake-Sleep for Models with Stochastic Control Flow
|
Stochastic control-flow models (SCFMs) are a class of generative models that involve branching on choices from discrete random variables. Amortized gradient-based learning of SCFMs is challenging as most approaches targeting discrete variables rely on their continuous relaxations---which can be intractable in SCFMs, as branching on relaxations requires evaluating all (exponentially many) branching paths. Tractable alternatives mainly combine REINFORCE with complex control-variate schemes to improve the variance of naive estimators. Here, we revisit the reweighted wake-sleep (RWS) (Bornschein and Bengio, 2015) algorithm, and through extensive evaluations, show that it outperforms current state-of-the-art methods in learning SCFMs. Further, in contrast to the importance weighted autoencoder, we observe that RWS learns better models and inference networks with increasing numbers of particles. Our results suggest that RWS is a competitive, often preferable, alternative for learning SCFMs.
|
Stochastic control-flow models (SCFMs) are a class of generative models that involve branching on choices from discrete random variables.
|
https://arxiv.org/abs/1805.10469v2
|
https://arxiv.org/pdf/1805.10469v2.pdf
|
ICLR 2019 5
|
[
"Tuan Anh Le",
"Adam R. Kosiorek",
"N. Siddharth",
"Yee Whye Teh",
"Frank Wood"
] |
[] | 2018-05-26T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": null,
"description": "**REINFORCE** is a Monte Carlo variant of a policy gradient algorithm in reinforcement learning. The agent collects samples of an episode using its current policy, and uses it to update the policy parameter $\\theta$. Since one full trajectory must be completed to construct a sample space, it is updated as an off-policy algorithm.\r\n\r\n$$ \\nabla\\_{\\theta}J\\left(\\theta\\right) = \\mathbb{E}\\_{\\pi}\\left[G\\_{t}\\nabla\\_{\\theta}\\ln\\pi\\_{\\theta}\\left(A\\_{t}\\mid{S\\_{t}}\\right)\\right]$$\r\n\r\nImage Credit: [Tingwu Wang](http://www.cs.toronto.edu/~tingwuwang/REINFORCE.pdf)",
"full_name": "REINFORCE",
"introduced_year": 1999,
"main_collection": {
"area": "Reinforcement Learning",
"description": "**Policy Gradient Methods** try to optimize the policy function directly in reinforcement learning. This contrasts with, for example, Q-Learning, where the policy manifests itself as maximizing a value function. Below you can find a continuously updating catalog of policy gradient methods.",
"name": "Policy Gradient Methods",
"parent": null
},
"name": "REINFORCE",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/universal-hypothesis-testing-with-kernels
|
1802.07581
| null | null |
Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit
|
We characterize the asymptotic performance of nonparametric goodness of fit
testing. The exponential decay rate of the type-II error probability is used as
the asymptotic performance metric, and a test is optimal if it achieves the
maximum rate subject to a constant level constraint on the type-I error
probability. We show that two classes of Maximum Mean Discrepancy (MMD) based
tests attain this optimality on $\mathbb R^d$, while the quadratic-time Kernel
Stein Discrepancy (KSD) based tests achieve the maximum exponential decay rate
under a relaxed level constraint. Under the same performance metric, we proceed
to show that the quadratic-time MMD based two-sample tests are also optimal for
general two-sample problems, provided that kernels are bounded continuous and
characteristic. Key to our approach are Sanov's theorem from large deviation
theory and the weak metrizable properties of the MMD and KSD.
| null |
http://arxiv.org/abs/1802.07581v3
|
http://arxiv.org/pdf/1802.07581v3.pdf
| null |
[
"Shengyu Zhu",
"Biao Chen",
"Pengfei Yang",
"Zhitang Chen"
] |
[
"Two-sample testing"
] | 2018-02-21T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "**Exponential Decay** is a learning rate schedule where we decay the learning rate with more iterations using an exponential function:\r\n\r\n$$ \\text{lr} = \\text{lr}\\_{0}\\exp\\left(-kt\\right) $$\r\n\r\nImage Credit: [Suki Lau](https://towardsdatascience.com/learning-rate-schedules-and-adaptive-learning-rate-methods-for-deep-learning-2c8f433990d1)",
"full_name": "Exponential Decay",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Learning Rate Schedules** refer to schedules for the learning rate during the training of neural networks. Below you can find a continuously updating list of learning rate schedules.",
"name": "Learning Rate Schedules",
"parent": null
},
"name": "Exponential Decay",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/sjtu-nlp-at-semeval-2018-task-9-neural
|
1805.10465
| null | null |
SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term Embeddings
|
This paper describes a hypernym discovery system for our participation in the
SemEval-2018 Task 9, which aims to discover the best (set of) candidate
hypernyms for input concepts or entities, given the search space of a
pre-defined vocabulary. We introduce a neural network architecture for the
concerned task and empirically study various neural network models to build the
representations in latent space for words and phrases. The evaluated models
include convolutional neural network, long-short term memory network, gated
recurrent unit and recurrent convolutional neural network. We also explore
different embedding methods, including word embedding and sense embedding for
better performance.
| null |
http://arxiv.org/abs/1805.10465v1
|
http://arxiv.org/pdf/1805.10465v1.pdf
|
SEMEVAL 2018 6
|
[
"Zhuosheng Zhang",
"Jiangtong Li",
"Hai Zhao",
"Bingjie Tang"
] |
[
"Hypernym Discovery"
] | 2018-05-26T00:00:00 |
https://aclanthology.org/S18-1147
|
https://aclanthology.org/S18-1147.pdf
|
sjtu-nlp-at-semeval-2018-task-9-neural-1
| null |
[] |
https://paperswithcode.com/paper/from-knowledge-graph-embedding-to-ontology
|
1805.10461
| null | null |
From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules
|
Recent years have witnessed the successful application of low-dimensional
vector space representations of knowledge graphs to predict missing facts or
find erroneous ones. However, it is not yet well-understood to what extent
ontological knowledge, e.g. given as a set of (existential) rules, can be
embedded in a principled way. To address this shortcoming, in this paper we
introduce a general framework based on a view of relations as regions, which
allows us to study the compatibility between ontological knowledge and
different types of vector space embeddings. Our technical contribution is
two-fold. First, we show that some of the most popular existing embedding
methods are not capable of modelling even very simple types of rules, which in
particular also means that they are not able to learn the type of dependencies
captured by such rules. Second, we study a model in which relations are
modelled as convex regions. We show particular that ontologies which are
expressed using so-called quasi-chained existential rules can be exactly
represented using convex regions, such that any set of facts which is induced
using that vector space embedding is logically consistent and deductively
closed with respect to the input ontology.
| null |
http://arxiv.org/abs/1805.10461v3
|
http://arxiv.org/pdf/1805.10461v3.pdf
| null |
[
"Víctor Gutiérrez-Basulto",
"Steven Schockaert"
] |
[
"Graph Embedding",
"Knowledge Graph Embedding",
"Knowledge Graphs",
"Ontology Embedding"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/lost-appearance-invariant-place-recognition
|
1804.05526
| null | null |
LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics
|
Human visual scene understanding is so remarkable that we are able to
recognize a revisited place when entering it from the opposite direction it was
first visited, even in the presence of extreme variations in appearance. This
capability is especially apparent during driving: a human driver can recognize
where they are when travelling in the reverse direction along a route for the
first time, without having to turn back and look. The difficulty of this
problem exceeds any addressed in past appearance- and viewpoint-invariant
visual place recognition (VPR) research, in part because large parts of the
scene are not commonly observable from opposite directions. Consequently, as
shown in this paper, the precision-recall performance of current
state-of-the-art viewpoint- and appearance-invariant VPR techniques is orders
of magnitude below what would be usable in a closed-loop system. Current
engineered solutions predominantly rely on panoramic camera or LIDAR sensing
setups; an eminently suitable engineering solution but one that is clearly very
different to how humans navigate, which also has implications for how naturally
humans could interact and communicate with the navigation system. In this paper
we develop a suite of novel semantic- and appearance-based techniques to enable
for the first time high performance place recognition in this challenging
scenario. We first propose a novel Local Semantic Tensor (LoST) descriptor of
images using the convolutional feature maps from a state-of-the-art dense
semantic segmentation network. Then, to verify the spatial semantic arrangement
of the top matching candidates, we develop a novel approach for mining
semantically-salient keypoint correspondences.
|
Human visual scene understanding is so remarkable that we are able to recognize a revisited place when entering it from the opposite direction it was first visited, even in the presence of extreme variations in appearance.
|
http://arxiv.org/abs/1804.05526v3
|
http://arxiv.org/pdf/1804.05526v3.pdf
| null |
[
"Sourav Garg",
"Niko Suenderhauf",
"Michael Milford"
] |
[
"Navigate",
"Scene Understanding",
"Semantic Segmentation",
"Visual Place Recognition"
] | 2018-04-16T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/intensive-preprocessing-of-kdd-cup-99-for
|
1805.10458
| null | null |
Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques
|
Network security engineers work to keep services available all the time by
handling intruder attacks. Intrusion Detection System (IDS) is one of the
obtainable mechanism that used to sense and classify any abnormal actions.
Therefore, the IDS must be always up to date with the latest intruder attacks
signatures to preserve confidentiality, integrity and availability of the
services. The speed of the IDS is very important issue as well learning the new
attacks. This research work illustrates how the Knowledge Discovery and Data
Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for
testing and evaluating different Machine Learning Techniques. It mainly focuses
on the KDD preprocess part in order to prepare a decent and fair experimental
data set. The techniques J48, Random Forest, Random Tree, MLP, Na\"ive Bayes
and Bayes Network classifiers have been chosen for this study. It has been
proven that the Random forest classifier has achieved the highest accuracy rate
for detecting and classifying all KDD dataset attacks, which are of type (DOS,
R2L, U2R, and PROBE)
| null |
http://arxiv.org/abs/1805.10458v2
|
http://arxiv.org/pdf/1805.10458v2.pdf
| null |
[
"Ibrahim Obeidat",
"Nabhan Hamadneh",
"Mouhammd Al-kasassbeh",
"Mohammad Almseidin"
] |
[
"BIG-bench Machine Learning",
"General Classification",
"Intrusion Detection"
] | 2018-05-26T00:00:00 | 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/fine-grained-age-estimation-in-the-wild-with
|
1805.10445
| null | null |
Fine-Grained Age Estimation in the wild with Attention LSTM Networks
|
Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision, which has a wide range of practical application values. Accuracy of age estimation of face images in the wild is relatively low for existing methods, because they only take into account the global features, while neglecting the fine-grained features of age-sensitive areas. We propose a novel method based on our attention long short-term memory (AL) network for fine-grained age estimation in the wild, inspired by the fine-grained categories and the visual attention mechanism. This method combines the residual networks (ResNets) or the residual network of residual network (RoR) models with LSTM units to construct AL-ResNets or AL-RoR networks to extract local features of age-sensitive regions, which effectively improves the age estimation accuracy. First, a ResNets or a RoR model pretrained on ImageNet dataset is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 dataset for age estimation. Then, we fine-tune the ResNets or the RoR on the target age datasets to extract the global features of face images. To extract the local features of age-sensitive regions, the LSTM unit is then presented to obtain the coordinates of the agesensitive region automatically. Finally, the age group classification is conducted directly on the Adience dataset, and age-regression experiments are performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and 15/16LAP datasets. By combining the global and the local features, we obtain our final prediction results. Experimental results illustrate the effectiveness and robustness of the proposed AL-ResNets or AL-RoR for age estimation in the wild, where it achieves better state-of-the-art performance than all other convolutional neural network.
| null |
https://arxiv.org/abs/1805.10445v2
|
https://arxiv.org/pdf/1805.10445v2.pdf
| null |
[
"Ke Zhang",
"Na Liu",
"Xingfang Yuan",
"Xinyao Guo",
"Ce Gao",
"Zhenbing Zhao",
"Zhanyu Ma"
] |
[
"Age And Gender Classification",
"Age Estimation",
"MORPH"
] | 2018-05-26T00:00:00 | 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
}
] |
https://paperswithcode.com/paper/deep-reinforcement-learning-for-resource
|
1805.06591
| null | null |
Deep Reinforcement Learning for Resource Management in Network Slicing
|
Network slicing is born as an emerging business to operators, by allowing
them to sell the customized slices to various tenants at different prices. In
order to provide better-performing and cost-efficient services, network slicing
involves challenging technical issues and urgently looks forward to intelligent
innovations to make the resource management consistent with users' activities
per slice. In that regard, deep reinforcement learning (DRL), which focuses on
how to interact with the environment by trying alternative actions and
reinforcing the tendency actions producing more rewarding consequences, is
assumed to be a promising solution. In this paper, after briefly reviewing the
fundamental concepts of DRL, we investigate the application of DRL in solving
some typical resource management for network slicing scenarios, which include
radio resource slicing and priority-based core network slicing, and demonstrate
the advantage of DRL over several competing schemes through extensive
simulations. Finally, we also discuss the possible challenges to apply DRL in
network slicing from a general perspective.
| null |
http://arxiv.org/abs/1805.06591v3
|
http://arxiv.org/pdf/1805.06591v3.pdf
| null |
[
"Rongpeng Li",
"Zhifeng Zhao",
"Qi Sun",
"Chi-Lin I",
"Chenyang Yang",
"Xianfu Chen",
"MinJian Zhao",
"Honggang Zhang"
] |
[
"Deep Reinforcement Learning",
"Management",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-17T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/posecnn-a-convolutional-neural-network-for-6d
|
1711.00199
| null | null |
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
|
Estimating the 6D pose of known objects is important for robots to interact
with the real world. The problem is challenging due to the variety of objects
as well as the complexity of a scene caused by clutter and occlusions between
objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network
for 6D object pose estimation. PoseCNN estimates the 3D translation of an
object by localizing its center in the image and predicting its distance from
the camera. The 3D rotation of the object is estimated by regressing to a
quaternion representation. We also introduce a novel loss function that enables
PoseCNN to handle symmetric objects. In addition, we contribute a large scale
video dataset for 6D object pose estimation named the YCB-Video dataset. Our
dataset provides accurate 6D poses of 21 objects from the YCB dataset observed
in 92 videos with 133,827 frames. We conduct extensive experiments on our
YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is
highly robust to occlusions, can handle symmetric objects, and provide accurate
pose estimation using only color images as input. When using depth data to
further refine the poses, our approach achieves state-of-the-art results on the
challenging OccludedLINEMOD dataset. Our code and dataset are available at
https://rse-lab.cs.washington.edu/projects/posecnn/.
|
We conduct extensive experiments on our YCB-Video dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input.
|
http://arxiv.org/abs/1711.00199v3
|
http://arxiv.org/pdf/1711.00199v3.pdf
| null |
[
"Yu Xiang",
"Tanner Schmidt",
"Venkatraman Narayanan",
"Dieter Fox"
] |
[
"6D Pose Estimation",
"6D Pose Estimation using RGB",
"6D Pose Estimation using RGBD",
"Object",
"Pose Estimation"
] | 2017-11-01T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/global-geometry-of-multichannel-sparse-blind
|
1805.10437
| null | null |
Multichannel Sparse Blind Deconvolution on the Sphere
|
Multichannel blind deconvolution is the problem of recovering an unknown
signal $f$ and multiple unknown channels $x_i$ from their circular convolution
$y_i=x_i \circledast f$ ($i=1,2,\dots,N$). We consider the case where the
$x_i$'s are sparse, and convolution with $f$ is invertible. Our nonconvex
optimization formulation solves for a filter $h$ on the unit sphere that
produces sparse output $y_i\circledast h$. Under some technical assumptions, we
show that all local minima of the objective function correspond to the inverse
filter of $f$ up to an inherent sign and shift ambiguity, and all saddle points
have strictly negative curvatures. This geometric structure allows successful
recovery of $f$ and $x_i$ using a simple manifold gradient descent (MGD)
algorithm. Our theoretical findings are complemented by numerical experiments,
which demonstrate superior performance of the proposed approach over the
previous methods.
| null |
http://arxiv.org/abs/1805.10437v2
|
http://arxiv.org/pdf/1805.10437v2.pdf
|
NeurIPS 2018 12
|
[
"Yanjun Li",
"Yoram Bresler"
] |
[] | 2018-05-26T00:00:00 | 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/nonlinear-variable-selection-with-continuous
|
1601.05285
| null | null |
Nonlinear variable selection with continuous outcome: a nonparametric incremental forward stagewise approach
|
We present a method of variable selection for the sparse generalized additive
model. The method doesn't assume any specific functional form, and can select
from a large number of candidates. It takes the form of incremental forward
stagewise regression. Given no functional form is assumed, we devised an
approach termed roughening to adjust the residuals in the iterations. In
simulations, we show the new method is competitive against popular machine
learning approaches. We also demonstrate its performance using some real
datasets. The method is available as a part of the nlnet package on CRAN
https://cran.r-project.org/package=nlnet.
| null |
http://arxiv.org/abs/1601.05285v4
|
http://arxiv.org/pdf/1601.05285v4.pdf
| null |
[
"Tianwei Yu"
] |
[
"BIG-bench Machine Learning",
"Form",
"regression",
"Variable Selection"
] | 2016-01-20T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/splitting-source-code-identifiers-using
|
1805.11651
| null | null |
Splitting source code identifiers using Bidirectional LSTM Recurrent Neural Network
|
Programmers make rich use of natural language in the source code they write
through identifiers and comments. Source code identifiers are selected from a
pool of tokens which are strongly related to the meaning, naming conventions,
and context. These tokens are often combined to produce more precise and
obvious designations. Such multi-part identifiers count for 97% of all naming
tokens in the Public Git Archive - the largest dataset of Git repositories to
date. We introduce a bidirectional LSTM recurrent neural network to detect
subtokens in source code identifiers. We trained that network on 41.7 million
distinct splittable identifiers collected from 182,014 open source projects in
Public Git Archive, and show that it outperforms several other machine learning
models. The proposed network can be used to improve the upstream models which
are based on source code identifiers, as well as improving developer experience
allowing writing code without switching the keyboard case.
| null |
http://arxiv.org/abs/1805.11651v2
|
http://arxiv.org/pdf/1805.11651v2.pdf
| null |
[
"Vadim Markovtsev",
"Waren Long",
"Egor Bulychev",
"Romain Keramitas",
"Konstantin Slavnov",
"Gabor Markowski"
] |
[] | 2018-05-26T00:00:00 | 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
}
] |
https://paperswithcode.com/paper/a-distributed-quasi-newton-algorithm-for
|
1803.01370
| null | null |
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization
|
We propose a communication- and computation-efficient distributed
optimization algorithm using second-order information for solving ERM problems
with a nonsmooth regularization term. Current second-order and quasi-Newton
methods for this problem either do not work well in the distributed setting or
work only for specific regularizers. Our algorithm uses successive quadratic
approximations, and we describe how to maintain an approximation of the Hessian
and solve subproblems efficiently in a distributed manner. The proposed method
enjoys global linear convergence for a broad range of non-strongly convex
problems that includes the most commonly used ERMs, thus requiring lower
communication complexity. It also converges on non-convex problems, so has the
potential to be used on applications such as deep learning. Initial
computational results on convex problems demonstrate that our method
significantly improves on communication cost and running time over the current
state-of-the-art methods.
|
Initial computational results on convex problems demonstrate that our method significantly improves on communication cost and running time over the current state-of-the-art methods.
|
http://arxiv.org/abs/1803.01370v2
|
http://arxiv.org/pdf/1803.01370v2.pdf
| null |
[
"Ching-pei Lee",
"Cong Han Lim",
"Stephen J. Wright"
] |
[
"Distributed Optimization"
] | 2018-03-04T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/a-novel-hybrid-score-level-and-decision-level
|
1805.10433
| null | null |
A novel hybrid score level and decision level fusion scheme for cancelable multi-biometric verification
|
In spite of the benefits of biometric-based authentication systems, there are
few concerns raised because of the sensitivity of biometric data to outliers,
low performance caused due to intra-class variations and privacy invasion
caused by information leakage. To address these issues, we propose a hybrid
fusion framework where only the protected modalities are combined to fulfill
the requirement of secrecy and performance improvement. This paper presents a
method to integrate cancelable modalities utilizing mean-closure weighting
(MCW) score level and Dempster-Shafer (DS) theory based decision level fusion
for iris and fingerprint to mitigate the limitations in the individual score or
decision fusion mechanisms. The proposed hybrid fusion scheme incorporates the
similarity scores from different matchers corresponding to each protected
modality. The individual scores obtained from different matchers for each
modality are combined using MCW score fusion method. The MCW technique achieves
the optimal weight for each matcher involved in the score computation. Further,
DS theory is applied to the induced scores to output the final decision. The
rigorous experimental evaluations on three virtual databases indicate that the
proposed hybrid fusion framework outperforms over the component level or
individual fusion methods (score level and decision level fusion). As a result,
we achieve (48%,66%), (72%,86%) and (49%,38%) of performance improvement over
unimodal cancelable iris and unimodal cancelable fingerprint verification
systems for Virtual_A, Virtual_B and Virtual_C databases, respectively. Also,
the proposed method is robust enough to the variability of scores and outliers
satisfying the requirement of secure authentication.
| null |
http://arxiv.org/abs/1805.10433v1
|
http://arxiv.org/pdf/1805.10433v1.pdf
| null |
[
"Rudresh Dwivedi",
"Somnath Dey"
] |
[
"fingerprint verification"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/model-based-pricing-for-machine-learning-in-a
|
1805.11450
| null | null |
Model-based Pricing for Machine Learning in a Data Marketplace
|
Data analytics using machine learning (ML) has become ubiquitous in science,
business intelligence, journalism and many other domains. While a lot of work
focuses on reducing the training cost, inference runtime and storage cost of ML
models, little work studies how to reduce the cost of data acquisition, which
potentially leads to a loss of sellers' revenue and buyers' affordability and
efficiency.
In this paper, we propose a model-based pricing (MBP) framework, which
instead of pricing the data, directly prices ML model instances. We first
formally describe the desired properties of the MBP framework, with a focus on
avoiding arbitrage. Next, we show a concrete realization of the MBP framework
via a noise injection approach, which provably satisfies the desired formal
properties. Based on the proposed framework, we then provide algorithmic
solutions on how the seller can assign prices to models under different market
scenarios (such as to maximize revenue). Finally, we conduct extensive
experiments, which validate that the MBP framework can provide high revenue to
the seller, high affordability to the buyer, and also operate on low runtime
cost.
| null |
http://arxiv.org/abs/1805.11450v1
|
http://arxiv.org/pdf/1805.11450v1.pdf
| null |
[
"Lingjiao Chen",
"Paraschos Koutris",
"Arun Kumar"
] |
[
"BIG-bench Machine Learning"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/robustness-analysis-of-visual-qa-models-by
|
1709.04625
| null | null |
Robustness Analysis of Visual QA Models by Basic Questions
|
Visual Question Answering (VQA) models should have both high robustness and
accuracy. Unfortunately, most of the current VQA research only focuses on
accuracy because there is a lack of proper methods to measure the robustness of
VQA models. There are two main modules in our algorithm. Given a natural
language question about an image, the first module takes the question as input
and then outputs the ranked basic questions, with similarity scores, of the
main given question. The second module takes the main question, image and these
basic questions as input and then outputs the text-based answer of the main
question about the given image. We claim that a robust VQA model is one, whose
performance is not changed much when related basic questions as also made
available to it as input. We formulate the basic questions generation problem
as a LASSO optimization, and also propose a large scale Basic Question Dataset
(BQD) and Rscore (novel robustness measure), for analyzing the robustness of
VQA models. We hope our BQD will be used as a benchmark for to evaluate the
robustness of VQA models, so as to help the community build more robust and
accurate VQA models.
| null |
http://arxiv.org/abs/1709.04625v3
|
http://arxiv.org/pdf/1709.04625v3.pdf
| null |
[
"Jia-Hong Huang",
"Cuong Duc Dao",
"Modar Alfadly",
"C. Huck Yang",
"Bernard Ghanem"
] |
[
"Question Answering",
"Visual Question Answering",
"Visual Question Answering (VQA)"
] | 2017-09-14T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/revisiting-video-saliency-a-large-scale
|
1801.07424
| null | null |
Revisiting Video Saliency: A Large-scale Benchmark and a New Model
|
In this work, we contribute to video saliency research in two ways. First, we
introduce a new benchmark for predicting human eye movements during dynamic
scene free-viewing, which is long-time urged in this field. Our dataset, named
DHF1K (Dynamic Human Fixation), consists of 1K high-quality, elaborately
selected video sequences spanning a large range of scenes, motions, object
types and background complexity. Existing video saliency datasets lack variety
and generality of common dynamic scenes and fall short in covering challenging
situations in unconstrained environments. In contrast, DHF1K makes a
significant leap in terms of scalability, diversity and difficulty, and is
expected to boost video saliency modeling. Second, we propose a novel video
saliency model that augments the CNN-LSTM network architecture with an
attention mechanism to enable fast, end-to-end saliency learning. The attention
mechanism explicitly encodes static saliency information, thus allowing LSTM to
focus on learning more flexible temporal saliency representation across
successive frames. Such a design fully leverages existing large-scale static
fixation datasets, avoids overfitting, and significantly improves training
efficiency and testing performance. We thoroughly examine the performance of
our model, with respect to state-of-the-art saliency models, on three
large-scale datasets (i.e., DHF1K, Hollywood2, UCF sports). Experimental
results over more than 1.2K testing videos containing 400K frames demonstrate
that our model outperforms other competitors.
|
Existing video saliency datasets lack variety and generality of common dynamic scenes and fall short in covering challenging situations in unconstrained environments.
|
http://arxiv.org/abs/1801.07424v3
|
http://arxiv.org/pdf/1801.07424v3.pdf
|
CVPR 2018 6
|
[
"Wenguan Wang",
"Jianbing Shen",
"Fang Guo",
"Ming-Ming Cheng",
"Ali Borji"
] |
[
"Video Saliency Detection"
] | 2018-01-23T00:00:00 |
http://openaccess.thecvf.com/content_cvpr_2018/html/Wang_Revisiting_Video_Saliency_CVPR_2018_paper.html
|
http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Revisiting_Video_Saliency_CVPR_2018_paper.pdf
|
revisiting-video-saliency-a-large-scale-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/towards-explanation-of-dnn-based-prediction
|
1804.00506
| null | null |
Towards Explanation of DNN-based Prediction with Guided Feature Inversion
|
While deep neural networks (DNN) have become an effective computational tool,
the prediction results are often criticized by the lack of interpretability,
which is essential in many real-world applications such as health informatics.
Existing attempts based on local interpretations aim to identify relevant
features contributing the most to the prediction of DNN by monitoring the
neighborhood of a given input. They usually simply ignore the intermediate
layers of the DNN that might contain rich information for interpretation. To
bridge the gap, in this paper, we propose to investigate a guided feature
inversion framework for taking advantage of the deep architectures towards
effective interpretation. The proposed framework not only determines the
contribution of each feature in the input but also provides insights into the
decision-making process of DNN models. By further interacting with the neuron
of the target category at the output layer of the DNN, we enforce the
interpretation result to be class-discriminative. We apply the proposed
interpretation model to different CNN architectures to provide explanations for
image data and conduct extensive experiments on ImageNet and PASCAL VOC07
datasets. The interpretation results demonstrate the effectiveness of our
proposed framework in providing class-discriminative interpretation for
DNN-based prediction.
| null |
http://arxiv.org/abs/1804.00506v2
|
http://arxiv.org/pdf/1804.00506v2.pdf
| null |
[
"Mengnan Du",
"Ninghao Liu",
"Qingquan Song",
"Xia Hu"
] |
[
"Decision Making"
] | 2018-03-19T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/enhanced-alignment-measure-for-binary
|
1805.10421
| null | null |
Enhanced-alignment Measure for Binary Foreground Map Evaluation
|
The existing binary foreground map (FM) measures to address various types of
errors in either pixel-wise or structural ways. These measures consider
pixel-level match or image-level information independently, while cognitive
vision studies have shown that human vision is highly sensitive to both global
information and local details in scenes. In this paper, we take a detailed look
at current binary FM evaluation measures and propose a novel and effective
E-measure (Enhanced-alignment measure). Our measure combines local pixel values
with the image-level mean value in one term, jointly capturing image-level
statistics and local pixel matching information. We demonstrate the superiority
of our measure over the available measures on 4 popular datasets via 5
meta-measures, including ranking models for applications, demoting generic,
random Gaussian noise maps, ground-truth switch, as well as human judgments. We
find large improvements in almost all the meta-measures. For instance, in terms
of application ranking, we observe improvementrangingfrom9.08% to 19.65%
compared with other popular measures.
|
The existing binary foreground map (FM) measures to address various types of errors in either pixel-wise or structural ways.
|
http://arxiv.org/abs/1805.10421v2
|
http://arxiv.org/pdf/1805.10421v2.pdf
| null |
[
"Deng-Ping Fan",
"Cheng Gong",
"Yang Cao",
"Bo Ren",
"Ming-Ming Cheng",
"Ali Borji"
] |
[] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/extracting-traffic-primitives-directly-from
|
1709.03553
| null | null |
Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications
|
Developing an automated vehicle, that can handle complicated driving
scenarios and appropriately interact with other road users, requires the
ability to semantically learn and understand driving environment, oftentimes,
based on analyzing massive amounts of naturalistic driving data. An important
paradigm that allows automated vehicles to both learn from human drivers and
gain insights is understanding the principal compositions of the entire
traffic, termed as traffic primitives. However, the exploding data growth
presents a great challenge in extracting primitives from high-dimensional
time-series traffic data with various types of road users engaged. Therefore,
automatically extracting primitives is becoming one of the cost-efficient ways
to help autonomous vehicles understand and predict the complex traffic
scenarios. In addition, the extracted primitives from raw data should 1) be
appropriate for automated driving applications and also 2) be easily used to
generate new traffic scenarios. However, existing literature does not provide a
method to automatically learn these primitives from large-scale traffic data.
The contribution of this paper has two manifolds. The first one is that we
proposed a new framework to generate new traffic scenarios from a handful of
limited traffic data. The second one is that we introduce a nonparametric
Bayesian learning method -- a sticky hierarchical Dirichlet process hidden
Markov model -- to automatically extract primitives from multidimensional
traffic data without prior knowledge of the primitive settings. The developed
method is then validated using one day of naturalistic driving data. Experiment
results show that the nonparametric Bayesian learning method is able to extract
primitives from traffic scenarios where both the binary and continuous events
coexist.
| null |
http://arxiv.org/abs/1709.03553v3
|
http://arxiv.org/pdf/1709.03553v3.pdf
| null |
[
"Wenshuo Wang",
"Ding Zhao"
] |
[
"Autonomous Vehicles",
"Time Series Analysis"
] | 2017-09-11T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/human-action-generation-with-generative
|
1805.10416
| null | null |
Human Action Generation with Generative Adversarial Networks
|
Inspired by the recent advances in generative models, we introduce a human
action generation model in order to generate a consecutive sequence of human
motions to formulate novel actions. We propose a framework of an autoencoder
and a generative adversarial network (GAN) to produce multiple and consecutive
human actions conditioned on the initial state and the given class label. The
proposed model is trained in an end-to-end fashion, where the autoencoder is
jointly trained with the GAN. The model is trained on the NTU RGB+D dataset and
we show that the proposed model can generate different styles of actions.
Moreover, the model can successfully generate a sequence of novel actions given
different action labels as conditions. The conventional human action prediction
and generation models lack those features, which are essential for practical
applications.
|
Inspired by the recent advances in generative models, we introduce a human action generation model in order to generate a consecutive sequence of human motions to formulate novel actions.
|
http://arxiv.org/abs/1805.10416v1
|
http://arxiv.org/pdf/1805.10416v1.pdf
| null |
[
"Mohammad Ahangar Kiasari",
"Dennis Singh Moirangthem",
"Minho Lee"
] |
[
"Action Generation",
"Generative Adversarial Network",
"Human action generation"
] | 2018-05-26T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a convolution allows for weight sharing - reducing the number of effective parameters - and image translation (allowing for the same feature to be detected in different parts of the input space).\r\n\r\nImage Source: [https://arxiv.org/pdf/1603.07285.pdf](https://arxiv.org/pdf/1603.07285.pdf)",
"full_name": "Convolution",
"introduced_year": 1980,
"main_collection": {
"area": "Computer Vision",
"description": "**Convolutions** are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and translation invariance. Below you can find a continuously updating list of convolutions.",
"name": "Convolutions",
"parent": "Image Feature Extractors"
},
"name": "Convolution",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "",
"description": "In today’s digital age, 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": "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/connecting-distant-entities-with-induction
|
1805.10414
| null | null |
Connecting Distant Entities with Induction through Conditional Random Fields for Named Entity Recognition: Precursor-Induced CRF
|
This paper presents a method of designing specific high-order dependency
factor on the linear chain conditional random fields (CRFs) for named entity
recognition (NER). Named entities tend to be separated from each other by
multiple outside tokens in a text, and thus the first-order CRF, as well as the
second-order CRF, may innately lose transition information between distant
named entities. The proposed design uses outside label in NER as a transmission
medium of precedent entity information on the CRF. Then, empirical results
apparently demonstrate that it is possible to exploit long-distance label
dependency in the original first-order linear chain CRF structure upon NER
while reducing computational loss rather than in the second-order CRF.
| null |
http://arxiv.org/abs/1805.10414v1
|
http://arxiv.org/pdf/1805.10414v1.pdf
|
WS 2018 7
|
[
"Wangjin Lee",
"Jinwook Choi"
] |
[
"named-entity-recognition",
"Named Entity Recognition",
"Named Entity Recognition (NER)",
"NER"
] | 2018-05-26T00:00:00 |
https://aclanthology.org/W18-2402
|
https://aclanthology.org/W18-2402.pdf
|
connecting-distant-entities-with-induction-1
| null |
[
{
"code_snippet_url": null,
"description": "**Conditional Random Fields** or **CRFs** are a type of probabilistic graph model that take neighboring sample context into account for tasks like classification. Prediction is modeled as a graphical model, which implements dependencies between the predictions. Graph choice depends on the application, for example linear chain CRFs are popular in natural language processing, whereas in image-based tasks, the graph would connect to neighboring locations in an image to enforce that they have similar predictions.\r\n\r\nImage Credit: [Charles Sutton and Andrew McCallum, An Introduction to Conditional Random Fields](https://homepages.inf.ed.ac.uk/csutton/publications/crftut-fnt.pdf)",
"full_name": "Conditional Random Field",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Structured Prediction** methods deal with structured outputs with multiple interdependent outputs. Below you can find a continuously updating list of structured prediction methods.",
"name": "Structured Prediction",
"parent": null
},
"name": "CRF",
"source_title": null,
"source_url": null
}
] |
https://paperswithcode.com/paper/classification-of-crystallization-outcomes
|
1803.10342
| null | null |
Classification of crystallization outcomes using deep convolutional neural networks
|
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has
assembled roughly half a million annotated images of macromolecular
crystallization experiments from various sources and setups. Here,
state-of-the-art machine learning algorithms are trained and tested on
different parts of this data set. We find that more than 94% of the test images
can be correctly labeled, irrespective of their experimental origin. Because
crystal recognition is key to high-density screening and the systematic
analysis of crystallization experiments, this approach opens the door to both
industrial and fundamental research applications.
|
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups.
|
http://arxiv.org/abs/1803.10342v2
|
http://arxiv.org/pdf/1803.10342v2.pdf
| null |
[
"Andrew E. Bruno",
"Patrick Charbonneau",
"Janet Newman",
"Edward H. Snell",
"David R. So",
"Vincent Vanhoucke",
"Christopher J. Watkins",
"Shawn Williams",
"Julie Wilson"
] |
[
"BIG-bench Machine Learning",
"Classification",
"General Classification"
] | 2018-03-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/fast-policy-learning-through-imitation-and
|
1805.10413
| null | null |
Fast Policy Learning through Imitation and Reinforcement
|
Imitation learning (IL) consists of a set of tools that leverage expert
demonstrations to quickly learn policies. However, if the expert is suboptimal,
IL can yield policies with inferior performance compared to reinforcement
learning (RL). In this paper, we aim to provide an algorithm that combines the
best aspects of RL and IL. We accomplish this by formulating several popular RL
and IL algorithms in a common mirror descent framework, showing that these
algorithms can be viewed as a variation on a single approach. We then propose
LOKI, a strategy for policy learning that first performs a small but random
number of IL iterations before switching to a policy gradient RL method. We
show that if the switching time is properly randomized, LOKI can learn to
outperform a suboptimal expert and converge faster than running policy gradient
from scratch. Finally, we evaluate the performance of LOKI experimentally in
several simulated environments.
| null |
http://arxiv.org/abs/1805.10413v1
|
http://arxiv.org/pdf/1805.10413v1.pdf
| null |
[
"Ching-An Cheng",
"Xinyan Yan",
"Nolan Wagener",
"Byron Boots"
] |
[
"Imitation Learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/3d-shape-segmentation-via-shape-fully
|
1702.08675
| null | null |
3D Shape Segmentation via Shape Fully Convolutional Networks
|
We desgin a novel fully convolutional network architecture for shapes,
denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented
as graph structures in the SFCN architecture, based on novel graph convolution
and pooling operations, which are similar to convolution and pooling operations
used on images. Meanwhile, to build our SFCN architecture in the original image
segmentation fully convolutional network (FCN) architecture, we also design and
implement a generating operation} with bridging function. This ensures that the
convolution and pooling operation we have designed can be successfully applied
in the original FCN architecture. In this paper, we also present a new shape
segmentation approach based on SFCN. Furthermore, we allow more general and
challenging input, such as mixed datasets of different categories of shapes}
which can prove the ability of our generalisation. In our approach, SFCNs are
trained triangles-to-triangles by using three low-level geometric features as
input. Finally, the feature voting-based multi-label graph cuts is adopted to
optimise the segmentation results obtained by SFCN prediction. The experiment
results show that our method can effectively learn and predict mixed shape
datasets of either similar or different characteristics, and achieve excellent
segmentation results.
| null |
http://arxiv.org/abs/1702.08675v3
|
http://arxiv.org/pdf/1702.08675v3.pdf
| null |
[
"Pengyu Wang",
"Yuan Gan",
"Panpan Shui",
"Fenggen Yu",
"Yan Zhang",
"Songle Chen",
"Zhengxing Sun"
] |
[
"Image Segmentation",
"Segmentation",
"Semantic Segmentation"
] | 2017-02-28T00:00:00 | 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/the-singular-values-of-convolutional-layers
|
1805.10408
| null |
rJevYoA9Fm
|
The Singular Values of Convolutional Layers
|
We characterize the singular values of the linear transformation associated
with a standard 2D multi-channel convolutional layer, enabling their efficient
computation. This characterization also leads to an algorithm for projecting a
convolutional layer onto an operator-norm ball. We show that this is an
effective regularizer; for example, it improves the test error of a deep
residual network using batch normalization on CIFAR-10 from 6.2\% to 5.3\%.
|
We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation.
|
http://arxiv.org/abs/1805.10408v2
|
http://arxiv.org/pdf/1805.10408v2.pdf
|
ICLR 2019 5
|
[
"Hanie Sedghi",
"Vineet Gupta",
"Philip M. Long"
] |
[] | 2018-05-26T00:00:00 |
https://openreview.net/forum?id=rJevYoA9Fm
|
https://openreview.net/pdf?id=rJevYoA9Fm
|
the-singular-values-of-convolutional-layers-1
| 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"
}
] |
https://paperswithcode.com/paper/semi-supervised-deep-kernel-learning
|
1805.10407
| null | null |
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
|
Large amounts of labeled data are typically required to train deep learning
models. For many real-world problems, however, acquiring additional data can be
expensive or even impossible. We present semi-supervised deep kernel learning
(SSDKL), a semi-supervised regression model based on minimizing predictive
variance in the posterior regularization framework. SSDKL combines the
hierarchical representation learning of neural networks with the probabilistic
modeling capabilities of Gaussian processes. By leveraging unlabeled data, we
show improvements on a diverse set of real-world regression tasks over
supervised deep kernel learning and semi-supervised methods such as VAT and
mean teacher adapted for regression.
|
Large amounts of labeled data are typically required to train deep learning models.
|
http://arxiv.org/abs/1805.10407v4
|
http://arxiv.org/pdf/1805.10407v4.pdf
|
NeurIPS 2018 12
|
[
"Neal Jean",
"Sang Michael Xie",
"Stefano Ermon"
] |
[
"Gaussian Processes",
"regression",
"Representation Learning"
] | 2018-05-26T00:00:00 |
http://papers.nips.cc/paper/7778-semi-supervised-deep-kernel-learning-regression-with-unlabeled-data-by-minimizing-predictive-variance
|
http://papers.nips.cc/paper/7778-semi-supervised-deep-kernel-learning-regression-with-unlabeled-data-by-minimizing-predictive-variance.pdf
|
semi-supervised-deep-kernel-learning-1
| null |
[] |
https://paperswithcode.com/paper/robust-nonparametric-regression-under-hubers
|
1805.10406
| null | null |
Robust Nonparametric Regression under Huber's $ε$-contamination Model
|
We consider the non-parametric regression problem under Huber's
$\epsilon$-contamination model, in which an $\epsilon$ fraction of observations
are subject to arbitrary adversarial noise. We first show that a simple local
binning median step can effectively remove the adversary noise and this median
estimator is minimax optimal up to absolute constants over the H\"{o}lder
function class with smoothness parameters smaller than or equal to 1.
Furthermore, when the underlying function has higher smoothness, we show that
using local binning median as pre-preprocessing step to remove the adversarial
noise, then we can apply any non-parametric estimator on top of the medians. In
particular we show local median binning followed by kernel smoothing and local
polynomial regression achieve minimaxity over H\"{o}lder and Sobolev classes
with arbitrary smoothness parameters. Our main proof technique is a decoupled
analysis of adversary noise and stochastic noise, which can be potentially
applied to other robust estimation problems. We also provide numerical results
to verify the effectiveness of our proposed methods.
| null |
http://arxiv.org/abs/1805.10406v1
|
http://arxiv.org/pdf/1805.10406v1.pdf
| null |
[
"Simon S. Du",
"Yining Wang",
"Sivaraman Balakrishnan",
"Pradeep Ravikumar",
"Aarti Singh"
] |
[
"model",
"regression"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/stochastic-subgradient-method-converges-on
|
1804.07795
| null | null |
Stochastic subgradient method converges on tame functions
|
This work considers the question: what convergence guarantees does the
stochastic subgradient method have in the absence of smoothness and convexity?
We prove that the stochastic subgradient method, on any semialgebraic locally
Lipschitz function, produces limit points that are all first-order stationary.
More generally, our result applies to any function with a Whitney stratifiable
graph. In particular, this work endows the stochastic subgradient method, and
its proximal extension, with rigorous convergence guarantees for a wide class
of problems arising in data science---including all popular deep learning
architectures.
|
This work considers the question: what convergence guarantees does the stochastic subgradient method have in the absence of smoothness and convexity?
|
http://arxiv.org/abs/1804.07795v3
|
http://arxiv.org/pdf/1804.07795v3.pdf
| null |
[
"Damek Davis",
"Dmitriy Drusvyatskiy",
"Sham Kakade",
"Jason D. Lee"
] |
[] | 2018-04-20T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/deep-convolutional-neural-networks-for-map
|
1805.10402
| null | null |
Deep Convolutional Neural Networks for Map-Type Classification
|
Maps are an important medium that enable people to comprehensively understand
the configuration of cultural activities and natural elements over different
times and places. Although massive maps are available in the digital era, how
to effectively and accurately access the required map remains a challenge
today. Previous works partially related to map-type classification mainly
focused on map comparison and map matching at the local scale. The features
derived from local map areas might be insufficient to characterize map content.
To facilitate establishing an automatic approach for accessing the needed map,
this paper reports our investigation into using deep learning techniques to
recognize seven types of map, including topographic map, terrain map, physical
map, urban scene map, the National Map, 3D map, nighttime map, orthophoto map,
and land cover classification map. Experimental results show that the
state-of-the-art deep convolutional neural networks can support automatic
map-type classification. Additionally, the classification accuracy varies
according to different map-types. We hope our work can contribute to the
implementation of deep learning techniques in cartographical community and
advance the progress of Geographical Artificial Intelligence (GeoAI).
| null |
http://arxiv.org/abs/1805.10402v1
|
http://arxiv.org/pdf/1805.10402v1.pdf
| null |
[
"Xiran Zhou",
"Wenwen Li",
"Samantha T. Arundel",
"Jun Liu"
] |
[
"Classification",
"General Classification",
"Land Cover Classification",
"Vocal Bursts Type Prediction"
] | 2018-05-26T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/toward-abstractive-summarization-using
|
1805.10399
| null | null |
Toward Abstractive Summarization Using Semantic Representations
|
We present a novel abstractive summarization framework that draws on the
recent development of a treebank for the Abstract Meaning Representation (AMR).
In this framework, the source text is parsed to a set of AMR graphs, the graphs
are transformed into a summary graph, and then text is generated from the
summary graph. We focus on the graph-to-graph transformation that reduces the
source semantic graph into a summary graph, making use of an existing AMR
parser and assuming the eventual availability of an AMR-to-text generator. The
framework is data-driven, trainable, and not specifically designed for a
particular domain. Experiments on gold-standard AMR annotations and system
parses show promising results. Code is available at:
https://github.com/summarization
|
We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR).
|
http://arxiv.org/abs/1805.10399v1
|
http://arxiv.org/pdf/1805.10399v1.pdf
|
HLT 2015 5
|
[
"Fei Liu",
"Jeffrey Flanigan",
"Sam Thomson",
"Norman Sadeh",
"Noah A. Smith"
] |
[
"Abstractive Text Summarization",
"Abstract Meaning Representation"
] | 2018-05-25T00:00:00 |
https://aclanthology.info/papers/N15-1114/n15-1114
|
https://www.aclweb.org/anthology/N15-1114
|
toward-abstractive-summarization-using-1
| null |
[] |
https://paperswithcode.com/paper/three-dimensional-radiotherapy-dose
|
1805.10397
| null | null |
Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture
|
The treatment planning process for patients with head and neck (H&N) cancer
is regarded as one of the most complicated due to large target volume, multiple
prescription dose levels, and many radiation-sensitive critical structures near
the target. Treatment planning for this site requires a high level of human
expertise and a tremendous amount of effort to produce personalized high
quality plans, taking as long as a week, which deteriorates the chances of
tumor control and patient survival. To solve this problem, we propose to
investigate a deep learning-based dose prediction model, Hierarchically Densely
Connected U-net, based on two highly popular network architectures: U-net and
DenseNet. We find that this new architecture is able to accurately and
efficiently predict the dose distribution, outperforming the other two models,
the Standard U-net and DenseNet, in homogeneity, dose conformity, and dose
coverage on the test data. Averaging across all organs at risk, our proposed
model is capable of predicting the organ-at-risk max dose within 6.3% and mean
dose within 5.1% of the prescription dose on the test data. The other models,
the Standard U-net and DenseNet, performed worse, having an averaged
organ-at-risk max dose prediction error of 8.2% and 9.3%, respectively, and
averaged mean dose prediction error of 6.4% and 6.8%, respectively. In
addition, our proposed model used 12 times less trainable parameters than the
Standard U-net, and predicted the patient dose 4 times faster than DenseNet.
| null |
http://arxiv.org/abs/1805.10397v3
|
http://arxiv.org/pdf/1805.10397v3.pdf
| null |
[
"Dan Nguyen",
"Xun Jia",
"David Sher",
"Mu-Han Lin",
"Zohaib Iqbal",
"Hui Liu",
"Steve Jiang"
] |
[] | 2018-05-25T00:00:00 | null | null | null | null |
[
{
"code_snippet_url": "https://github.com/milesial/Pytorch-UNet/blob/67bf11b4db4c5f2891bd7e8e7f58bcde8ee2d2db/unet/unet_model.py#L8",
"description": "**U-Net** is an architecture for semantic segmentation. It consists of a contracting path and an expansive path. The contracting path follows the typical architecture of a convolutional network. It consists of the repeated application of two 3x3 convolutions (unpadded convolutions), each followed by a rectified linear unit ([ReLU](https://paperswithcode.com/method/relu)) and a 2x2 [max pooling](https://paperswithcode.com/method/max-pooling) operation with stride 2 for downsampling. At each downsampling step we double the number of feature channels. Every step in the expansive path consists of an upsampling of the feature map followed by a 2x2 [convolution](https://paperswithcode.com/method/convolution) (“up-convolution”) that halves the number of feature channels, a concatenation with the correspondingly cropped feature map from the contracting path, and two 3x3 convolutions, each followed by a ReLU. The cropping is necessary due to the loss of border pixels in every convolution. At the final layer a [1x1 convolution](https://paperswithcode.com/method/1x1-convolution) is used to map each 64-component feature vector to the desired number of classes. In total the network has 23 convolutional layers.\r\n\r\n[Original MATLAB Code](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/u-net-release-2015-10-02.tar.gz)",
"full_name": "U-Net",
"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": "U-Net",
"source_title": "U-Net: Convolutional Networks for Biomedical Image Segmentation",
"source_url": "http://arxiv.org/abs/1505.04597v1"
},
{
"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/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": "",
"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": "**Average Pooling** is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - meaning translating the image by a small amount does not significantly affect the values of most pooled outputs. It extracts features more smoothly than [Max Pooling](https://paperswithcode.com/method/max-pooling), whereas max pooling extracts more pronounced features like edges.\r\n\r\nImage Source: [here](https://www.researchgate.net/figure/Illustration-of-Max-Pooling-and-Average-Pooling-Figure-2-above-shows-an-example-of-max_fig2_333593451)",
"full_name": "Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Average Pooling",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/densenet.py#L113",
"description": "A **Concatenated Skip Connection** is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more information to be retained from previous layers of the network. This contrasts with say, residual connections, where element-wise summation is used instead to incorporate information from previous layers. This type of skip connection is prominently used in DenseNets (and also Inception networks), which the Figure to the right illustrates.",
"full_name": "Concatenated Skip Connection",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Skip Connections** allow layers to skip layers and connect to layers further up the network, allowing for information to flow more easily up the network. Below you can find a continuously updating list of skip connection methods.",
"name": "Skip Connections",
"parent": null
},
"name": "Concatenated Skip Connection",
"source_title": null,
"source_url": null
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/baa592b215804927e28638f6a7f3318cbc411d49/torchvision/models/resnet.py#L157",
"description": "**Global Average Pooling** is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding category of the classification task in the last mlpconv layer. Instead of adding fully connected layers on top of the feature maps, we take the average of each feature map, and the resulting vector is fed directly into the [softmax](https://paperswithcode.com/method/softmax) layer. \r\n\r\nOne advantage of global [average pooling](https://paperswithcode.com/method/average-pooling) over the fully connected layers is that it is more native to the [convolution](https://paperswithcode.com/method/convolution) structure by enforcing correspondences between feature maps and categories. Thus the feature maps can be easily interpreted as categories confidence maps. Another advantage is that there is no parameter to optimize in the global average pooling thus overfitting is avoided at this layer. Furthermore, global average pooling sums out the spatial information, thus it is more robust to spatial translations of the input.",
"full_name": "Global Average Pooling",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Pooling Operations** are used to pool features together, often downsampling the feature map to a smaller size. They can also induce favourable properties such as translation invariance in image classification, as well as bring together information from different parts of a network in tasks like object detection (e.g. pooling different scales). ",
"name": "Pooling Operations",
"parent": null
},
"name": "Global Average Pooling",
"source_title": "Network In Network",
"source_url": "http://arxiv.org/abs/1312.4400v3"
},
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/1aef87d01eec2c0989458387fa04baebcc86ea7b/torchvision/models/densenet.py#L93",
"description": "A **Dense Block** is a module used in convolutional neural networks that connects *all layers* (with matching feature-map sizes) directly with each other. It was originally proposed as part of the [DenseNet](https://paperswithcode.com/method/densenet) architecture. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers. In contrast to [ResNets](https://paperswithcode.com/method/resnet), we never combine features through summation before they are passed into a layer; instead, we combine features by concatenating them. Hence, the $\\ell^{th}$ layer has $\\ell$ inputs, consisting of the feature-maps of all preceding convolutional blocks. Its own feature-maps are passed on to all $L-\\ell$ subsequent layers. This introduces $\\frac{L(L+1)}{2}$ connections in an $L$-layer network, instead of just $L$, as in traditional architectures: \"dense connectivity\".",
"full_name": "Dense Block",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Model Blocks** are building blocks used in image models such as convolutional neural networks. Below you can find a continuously updating list of image model blocks.",
"name": "Image Model Blocks",
"parent": null
},
"name": "Dense Block",
"source_title": "Densely Connected Convolutional Networks",
"source_url": "http://arxiv.org/abs/1608.06993v5"
},
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/0adb5843766092fba584791af76383125fd0d01c/torch/nn/init.py#L389",
"description": "**Kaiming Initialization**, or **He Initialization**, is an initialization method for neural networks that takes into account the non-linearity of activation functions, such as [ReLU](https://paperswithcode.com/method/relu) activations.\r\n\r\nA proper initialization method should avoid reducing or magnifying the magnitudes of input signals exponentially. Using a derivation they work out that the condition to stop this happening is:\r\n\r\n$$\\frac{1}{2}n\\_{l}\\text{Var}\\left[w\\_{l}\\right] = 1 $$\r\n\r\nThis implies an initialization scheme of:\r\n\r\n$$ w\\_{l} \\sim \\mathcal{N}\\left(0, 2/n\\_{l}\\right)$$\r\n\r\nThat is, a zero-centered Gaussian with standard deviation of $\\sqrt{2/{n}\\_{l}}$ (variance shown in equation above). Biases are initialized at $0$.",
"full_name": "Kaiming Initialization",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Initialization** methods are used to initialize the weights in a neural network. Below can you find a continuously updating list of initialization methods.",
"name": "Initialization",
"parent": null
},
"name": "Kaiming Initialization",
"source_title": "Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification",
"source_url": "http://arxiv.org/abs/1502.01852v1"
},
{
"code_snippet_url": "",
"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/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": "",
"description": "In today’s digital age, XRP 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 XRP transaction not confirmed, your XRP wallet not showing balance, or you're trying to recover a lost XRP wallet, knowing where to get help is essential. That’s why the XRP 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 XRP Customer Support Number +1-833-534-1729\r\nXRP 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. XRP Transaction Not Confirmed\r\nOne of the most common concerns is when a XRP 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. XRP 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 XRP 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 XRP 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 XRP 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. XRP Deposit Not Received\r\nIf someone has sent you XRP 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 XRP 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. XRP Transaction Stuck or Pending\r\nSometimes your XRP 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. XRP Wallet Recovery Phrase Issue\r\nYour 12 or 24-word XRP 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 XRP 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 XRP tech.\r\n\r\n24/7 Availability: XRP 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 XRP Support and Wallet Issues\r\nQ1: Can XRP support help me recover stolen BTC?\r\nA: While XRP 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: XRP 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 XRP’s official number (XRP is decentralized), it connects you to trained professionals experienced in resolving all major XRP issues.\r\n\r\nFinal Thoughts\r\nXRP 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 XRP transaction not confirmed, your XRP wallet not showing balance, or you're battling with a wallet recovery phrase issue, calling the XRP 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": "XRP Customer Service Number +1-833-534-1729",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "If you have questions or want to make special travel arrangements, you can make them online or call ☎️+1-801-(855)-(5905)or +1-804-853-9001✅. For hearing or speech impaired assistance dial 711 to be connected through the National Relay Service.",
"name": "Convolutional Neural Networks",
"parent": "Image Models"
},
"name": "XRP Customer Service Number +1-833-534-1729",
"source_title": "Densely Connected Convolutional Networks",
"source_url": "http://arxiv.org/abs/1608.06993v5"
}
] |
https://paperswithcode.com/paper/an-improved-phrase-based-approach-to
|
1805.10396
| null | null |
An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses
|
Teaching large classes remains a great challenge, primarily because it is
difficult to attend to all the student needs in a timely manner. Automatic text
summarization systems can be leveraged to summarize the student feedback,
submitted immediately after each lecture, but it is left to be discovered what
makes a good summary for student responses. In this work we explore a new
methodology that effectively extracts summary phrases from the student
responses. Each phrase is tagged with the number of students who raise the
issue. The phrases are evaluated along two dimensions: with respect to text
content, they should be informative and well-formed, measured by the ROUGE
metric; additionally, they shall attend to the most pressing student needs,
measured by a newly proposed metric. This work is enabled by a phrase-based
annotation and highlighting scheme, which is new to the summarization task. The
phrase-based framework allows us to summarize the student responses into a set
of bullet points and present to the instructor promptly.
| null |
http://arxiv.org/abs/1805.10396v1
|
http://arxiv.org/pdf/1805.10396v1.pdf
|
COLING 2016 12
|
[
"Wencan Luo",
"Fei Liu",
"Diane Litman"
] |
[
"Text Summarization"
] | 2018-05-25T00:00:00 |
https://aclanthology.org/C16-1006
|
https://aclanthology.org/C16-1006.pdf
|
an-improved-phrase-based-approach-to-2
| null |
[] |
https://paperswithcode.com/paper/automatic-summarization-of-student-course
|
1805.10395
| null | null |
Automatic Summarization of Student Course Feedback
|
Student course feedback is generated daily in both classrooms and online
course discussion forums. Traditionally, instructors manually analyze these
responses in a costly manner. In this work, we propose a new approach to
summarizing student course feedback based on the integer linear programming
(ILP) framework. Our approach allows different student responses to share
co-occurrence statistics and alleviates sparsity issues. Experimental results
on a student feedback corpus show that our approach outperforms a range of
baselines in terms of both ROUGE scores and human evaluation.
| null |
http://arxiv.org/abs/1805.10395v1
|
http://arxiv.org/pdf/1805.10395v1.pdf
|
NAACL 2016 6
|
[
"Wencan Luo",
"Fei Liu",
"Zitao Liu",
"Diane Litman"
] |
[] | 2018-05-25T00:00:00 |
https://aclanthology.org/N16-1010
|
https://aclanthology.org/N16-1010.pdf
|
automatic-summarization-of-student-course-1
| null |
[] |
https://paperswithcode.com/paper/dvae-discrete-variational-autoencoders-with-1
|
1802.04920
| null | null |
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
|
Training of discrete latent variable models remains challenging because
passing gradient information through discrete units is difficult. We propose a
new class of smoothing transformations based on a mixture of two overlapping
distributions, and show that the proposed transformation can be used for
training binary latent models with either directed or undirected priors. We
derive a new variational bound to efficiently train with Boltzmann machine
priors. Using this bound, we develop DVAE++, a generative model with a global
discrete prior and a hierarchy of convolutional continuous variables.
Experiments on several benchmarks show that overlapping transformations
outperform other recent continuous relaxations of discrete latent variables
including Gumbel-Softmax (Maddison et al., 2016; Jang et al., 2016), and
discrete variational autoencoders (Rolfe 2016).
|
Training of discrete latent variable models remains challenging because passing gradient information through discrete units is difficult.
|
http://arxiv.org/abs/1802.04920v2
|
http://arxiv.org/pdf/1802.04920v2.pdf
|
ICML 2018 7
|
[
"Arash Vahdat",
"William G. Macready",
"Zhengbing Bian",
"Amir Khoshaman",
"Evgeny Andriyash"
] |
[
"Image Generation"
] | 2018-02-14T00:00:00 |
https://icml.cc/Conferences/2018/Schedule?showEvent=2273
|
http://proceedings.mlr.press/v80/vahdat18a/vahdat18a.pdf
|
dvae-discrete-variational-autoencoders-with-2
| null |
[] |
https://paperswithcode.com/paper/a-mathematical-framework-for-deep-learning-in
|
1802.10055
| null | null |
A Mathematical Framework for Deep Learning in Elastic Source Imaging
|
An inverse elastic source problem with sparse measurements is of concern. A
generic mathematical framework is proposed which incorporates a low-
dimensional manifold regularization in the conventional source reconstruction
algorithms thereby enhancing their performance with sparse datasets. It is
rigorously established that the proposed framework is equivalent to the
so-called \emph{deep convolutional framelet expansion} in machine learning
literature for inverse problems. Apposite numerical examples are furnished to
substantiate the efficacy of the proposed framework.
| null |
http://arxiv.org/abs/1802.10055v3
|
http://arxiv.org/pdf/1802.10055v3.pdf
| null |
[
"Jaejun Yoo",
"Abdul Wahab",
"Jong Chul Ye"
] |
[
"BIG-bench Machine Learning",
"Deep Learning"
] | 2018-02-27T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/modeling-language-vagueness-in-privacy
|
1805.10393
| null | null |
Modeling Language Vagueness in Privacy Policies using Deep Neural Networks
|
Website privacy policies are too long to read and difficult to understand.
The over-sophisticated language makes privacy notices to be less effective than
they should be. People become even less willing to share their personal
information when they perceive the privacy policy as vague. This paper focuses
on decoding vagueness from a natural language processing perspective. While
thoroughly identifying the vague terms and their linguistic scope remains an
elusive challenge, in this work we seek to learn vector representations of
words in privacy policies using deep neural networks. The vector
representations are fed to an interactive visualization tool (LSTMVis) to test
on their ability to discover syntactically and semantically related vague
terms. The approach holds promise for modeling and understanding language
vagueness.
| null |
http://arxiv.org/abs/1805.10393v1
|
http://arxiv.org/pdf/1805.10393v1.pdf
| null |
[
"Fei Liu",
"Nicole Lee Fella",
"Kexin Liao"
] |
[] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
https://paperswithcode.com/paper/reinforced-extractive-summarization-with
|
1805.10392
| null | null |
Reinforced Extractive Summarization with Question-Focused Rewards
|
We investigate a new training paradigm for extractive summarization.
Traditionally, human abstracts are used to derive goldstandard labels for
extraction units. However, the labels are often inaccurate, because human
abstracts and source documents cannot be easily aligned at the word level. In
this paper we convert human abstracts to a set of Cloze-style comprehension
questions. System summaries are encouraged to preserve salient source content
useful for answering questions and share common words with the abstracts. We
use reinforcement learning to explore the space of possible extractive
summaries and introduce a question-focused reward function to promote concise,
fluent, and informative summaries. Our experiments show that the proposed
method is effective. It surpasses state-of-the-art systems on the standard
summarization dataset.
| null |
http://arxiv.org/abs/1805.10392v2
|
http://arxiv.org/pdf/1805.10392v2.pdf
|
ACL 2018 7
|
[
"Kristjan Arumae",
"Fei Liu"
] |
[
"Extractive Summarization",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-25T00:00:00 |
https://aclanthology.org/P18-3015
|
https://aclanthology.org/P18-3015.pdf
|
reinforced-extractive-summarization-with-1
| null |
[] |
https://paperswithcode.com/paper/toward-extractive-summarization-of-online
|
1805.10390
| null | null |
Toward Extractive Summarization of Online Forum Discussions via Hierarchical Attention Networks
|
Forum threads are lengthy and rich in content. Concise thread summaries will
benefit both newcomers seeking information and those who participate in the
discussion. Few studies, however, have examined the task of forum thread
summarization. In this work we make the first attempt to adapt the hierarchical
attention networks for thread summarization. The model draws on the recent
development of neural attention mechanisms to build sentence and thread
representations and use them for summarization. Our results indicate that the
proposed approach can outperform a range of competitive baselines. Further, a
redundancy removal step is crucial for achieving outstanding results.
| null |
http://arxiv.org/abs/1805.10390v2
|
http://arxiv.org/pdf/1805.10390v2.pdf
| null |
[
"Sansiri Tarnpradab",
"Fei Liu",
"Kien A. Hua"
] |
[
"Extractive Summarization",
"Sentence"
] | 2018-05-25T00:00:00 | null | null | null | null |
[] |
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