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Neural Topic Model via Optimal Transport | 1 INTRODUCTION . As an unsupervised approach , topic modelling has enjoyed great success in automatic text analysis . In general , a topic model aims to discover a set of latent topics from a collection of documents , each of which describes an interpretable semantic concept . Topic models like Latent Dirichlet Allocat... | The paper proposes a neural topic model derived from the perspective of optimal transport (OT). Topic embeddings are learned as part of the training process and is used to construct the cost matrix of the transport. The cost function based on the OT distance is further improved by combining with the cross-entropy loss... | SP:a020f6bca5d85f83d595e5b724e32394009dcd7e |
Memformer: The Memory-Augmented Transformer | 1 INTRODUCTION . Memory has a fundamental role in human cognition . Humans perceive and encode sensory information into a compressed representation in neurons , and later our brains can effectively retrieve past information to accomplish various tasks . The formation of memories involves complex cognitive processes . M... | This paper proposes a new style transformer with external memory, which is updated and used through an attention mechanism. They also propose a new algorithm to train the memory, Memory Replay Back-Propagation (MRBP). The memory consists of key-value pair data and is recurrently updated after the segment encoding. Thro... | SP:1ea373170ff80da65268e36e30370f2116fa4ed3 |
Memformer: The Memory-Augmented Transformer | 1 INTRODUCTION . Memory has a fundamental role in human cognition . Humans perceive and encode sensory information into a compressed representation in neurons , and later our brains can effectively retrieve past information to accomplish various tasks . The formation of memories involves complex cognitive processes . M... | The paper presents a new model for the task of language modeling especially suited for longer sequences. This new model dubbed as Memformer consists of Transformer encoder-decoder and a memory module to store the past information from the encoder outputs. The encoder bidirectionally attends to the immediate previous se... | SP:1ea373170ff80da65268e36e30370f2116fa4ed3 |
Diffeomorphic Template Transformers | 1 INTRODUCTION . The success of Convolutional Neural Networks ( CNNs ) in many modeling tasks is often attributed to their depth and inductive bias . An important inductive bias in CNNs is spatial symmetry ( e.g . translational equivariance ) which are embedded in the architecture through weight-sharing constraints . A... | The authors present a modification to spatial transformer networks that restricts the transformations to the group of diffeomorphisms. When combined with shape priors, this imposes topological constraints on the mappings produced by the network. These are important considerations in applications such as segmentation ta... | SP:f010fddc7ee6523ff0afa0ea2b9e1a55027b09de |
Diffeomorphic Template Transformers | 1 INTRODUCTION . The success of Convolutional Neural Networks ( CNNs ) in many modeling tasks is often attributed to their depth and inductive bias . An important inductive bias in CNNs is spatial symmetry ( e.g . translational equivariance ) which are embedded in the architecture through weight-sharing constraints . A... | This paper propose a novel method to incorporate shape prior in neural networks based on Diffeomorphic transformation. This is useful as by design it preserves certain desirable properties of output such as smooth boundaries and connected components which are of interest in medical imaging applications. The method is... | SP:f010fddc7ee6523ff0afa0ea2b9e1a55027b09de |
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability | 1 INTRODUCTION . Neural networks are almost never trained using ( full-batch ) gradient descent , even though gradient descent is the conceptual basis for popular optimization algorithms such as SGD . In this paper , we train neural networks using gradient descent , and find two surprises . First , while little is know... | This submission numerically shows that during exploring the neural network landscape, GD flow keeps increasing the sharpness. As a result, GD with a fixed learning rate will exhibit two phases during the dynamics. Denote by $\eta$ the fixed learning rate. In the first phase, GD follows closely to the GD flow, and i... | SP:d442ae98d8f485119b8fdd7070d16a7cabc0f9ea |
Gradient Descent on Neural Networks Typically Occurs at the Edge of Stability | 1 INTRODUCTION . Neural networks are almost never trained using ( full-batch ) gradient descent , even though gradient descent is the conceptual basis for popular optimization algorithms such as SGD . In this paper , we train neural networks using gradient descent , and find two surprises . First , while little is know... | This paper presents an interesting observation for GD. That is, the sharpness of the learnt model in the final phase of the training (measured by the largest eigenvalue of the training loss Hessian) hovers right at the value 2/\eta while the training loss. At the same time, the loss goes to unstable and non-monotonical... | SP:d442ae98d8f485119b8fdd7070d16a7cabc0f9ea |
A Critical Analysis of Distribution Shift | 1 INTRODUCTION . While the research community must create robust models that generalize to new scenarios , the robustness literature ( Dodge and Karam , 2017 ; Geirhos et al. , 2020 ) lacks consensus on evaluation benchmarks and contains many dissonant hypotheses . Hendrycks et al . ( 2020a ) find that many recent lang... | This paper provides a empirical study on the robustness of image classification models to distributions shifts. The authors construct three benchmark datasets that control for effects like artistic renditions of common classes, view-point changes, and geographic shifts (among others). The datasets are then used to test... | SP:707b1ba524c785d8942517ba7dff17115012181f |
A Critical Analysis of Distribution Shift | 1 INTRODUCTION . While the research community must create robust models that generalize to new scenarios , the robustness literature ( Dodge and Karam , 2017 ; Geirhos et al. , 2020 ) lacks consensus on evaluation benchmarks and contains many dissonant hypotheses . Hendrycks et al . ( 2020a ) find that many recent lang... | This paper investigates the robustness problem of computer vision model. To study the model robustness in a controlled setting, the author introduces three new robustness benchmarks: ImageNet-R, StreetView StoreFronts and DeepFashion Remixed. Each of them address different aspects of distribution drift in the real worl... | SP:707b1ba524c785d8942517ba7dff17115012181f |
Symmetric Wasserstein Autoencoders | 1 INTRODUCTION . Deep generative models have emerged as powerful frameworks for modelling complex data . Widely used families of such models include Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) , Variational Autoencoders ( VAEs ) ( Rezende et al. , 2014 ; Kingma & Welling , 2014 ) , and autoreg... | This works proposes an new auto-encoder variant based on an Optimal Transport (OT) penalty. While there are many such previous works of OT and auto-encoders, this work proposes a joint OT penalty on data and latent space. As the scalability of computing OT penalties in high dimensions is a concern, the authors address... | SP:1c4488d4b73efbed04b1045b425d7804b405ce1f |
Symmetric Wasserstein Autoencoders | 1 INTRODUCTION . Deep generative models have emerged as powerful frameworks for modelling complex data . Widely used families of such models include Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) , Variational Autoencoders ( VAEs ) ( Rezende et al. , 2014 ; Kingma & Welling , 2014 ) , and autoreg... | This paper proposes to treat the encoding and the decoding pairs symmetrically as a solution to OT problems. SWAE minimizes $p(x_d, z_d)$ and $p(x_e, z_e)$ in a jointly manner and shows better latent representation learning and generation. Moreover, the symmetric treatment for encoding and decoding shows an advantage i... | SP:1c4488d4b73efbed04b1045b425d7804b405ce1f |
Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw | 1 INTRODUCTION . Self-supervised learning ( SSL ) has achieved tremendous successes recently for static images ( He et al. , 2020 ; Chen et al. , 2020 ) and shown to be able to outperform supervised learning on a wide range of downstream image understanding tasks . However , such successes have not yet been reproduced ... | In this paper, the authors extend the self-supervised 2D jigsaw puzzle solving idea to 3D for self-supervised video representation learning. To make the 3D jigsaw puzzle problem tractable, they propose a two-fold idea. First, they constrain the 3D jigsaw puzzle solution space by factorizing the permutations into time, ... | SP:89dc84f203effa2b434cdf323ff251043336754e |
Self-Supervised Video Representation Learning with Constrained Spatiotemporal Jigsaw | 1 INTRODUCTION . Self-supervised learning ( SSL ) has achieved tremendous successes recently for static images ( He et al. , 2020 ; Chen et al. , 2020 ) and shown to be able to outperform supervised learning on a wide range of downstream image understanding tasks . However , such successes have not yet been reproduced ... | The paper presents a novel pretext task for self-supervised video representation learning (SSVRL). The authors design several surrogate tasks for tackling intentionally constructed constrained spatiotemporal jigsaw puzzles. The learned representations during training to solve the surrogate tasks can be transferred to ... | SP:89dc84f203effa2b434cdf323ff251043336754e |
Efficient Graph Neural Architecture Search | Recently , graph neural networks ( GNN ) have been demonstrated effective in various graph-based tasks . To obtain state-of-the-art ( SOTA ) data-specific GNN architectures , researchers turn to the neural architecture search ( NAS ) methods . However , it remains to be a challenging problem to conduct efficient archit... | The paper proposes a framework for efficient architecture search for graphs. This is done by combining a differentiable DARTS-like architecture encoding with a transfer learning method, that searches on smaller graphs with similar properties, and then transfers to the target graphs. The experiments show that EGAN match... | SP:02c82e31ddcff1990d5cb3f8ecbb44392cb02892 |
Efficient Graph Neural Architecture Search | Recently , graph neural networks ( GNN ) have been demonstrated effective in various graph-based tasks . To obtain state-of-the-art ( SOTA ) data-specific GNN architectures , researchers turn to the neural architecture search ( NAS ) methods . However , it remains to be a challenging problem to conduct efficient archit... | This work proposes an efficient graph neural architecture search to address the problem of automatically designing GNN architecture for any graph-based task. Comparing with the existing NAS approaches for GNNs, the authors improves the search efficiency from the following three components: (1) a slim search space only ... | SP:02c82e31ddcff1990d5cb3f8ecbb44392cb02892 |
Deep Ensembles with Hierarchical Diversity Pruning | 1 INTRODUCTION . Deep ensembles with sufficient ensemble diversity hold potential of improving both accuracy and robustness of ensembles with their combined wisdom . The improvement can be measured by three criteria : ( i ) the average ensemble accuracy of the selected ensemble teams , ( ii ) the percentage of selected... | The manuscript studies the problem of ensemble selection (pruning) with the ensemble consists of deep neural network models. The authors compare different diversity metrics, which they named collectively as Q-metric, visualize the accuracies of different ensembles on CIFAR-10 dataset where the ensembles are stratified ... | SP:7bcf05b89cb5776ae03592d5619d859e5c8571bc |
Deep Ensembles with Hierarchical Diversity Pruning | 1 INTRODUCTION . Deep ensembles with sufficient ensemble diversity hold potential of improving both accuracy and robustness of ensembles with their combined wisdom . The improvement can be measured by three criteria : ( i ) the average ensemble accuracy of the selected ensemble teams , ( ii ) the percentage of selected... | The paper succeeds in developing diversity metrics that correlate better with ensemble accuracy than the original diversity metrics. However, this makes one wonder why one cannot just use ensemble accuracy directly. One can also use a combining scheme along the lines of (Freund, 1995) where it adds models that focus on... | SP:7bcf05b89cb5776ae03592d5619d859e5c8571bc |
Stochastic Normalized Gradient Descent with Momentum for Large Batch Training | 1 INTRODUCTION . In machine learning , we often need to solve the following empirical risk minimization problem : min w∈Rd F ( w ) = 1 n n∑ i=1 fi ( w ) , ( 1 ) where w ∈ Rd denotes the model parameter , n denotes the number of training samples , fi ( w ) denotes the loss on the ith training sample . The problem in ( 1... | This paper proposes a new stochastic normalized gradient descent method with momentum (SNGM) for large batch training. They prove that unlike mometum SGD (MSGD), SNGM can adopt larger batch size to converge to the epsilon-stationary point with the same computation complexity (total number of gradient computation). The ... | SP:276a1974451e9740ff761c45ff63de47aabe0534 |
Stochastic Normalized Gradient Descent with Momentum for Large Batch Training | 1 INTRODUCTION . In machine learning , we often need to solve the following empirical risk minimization problem : min w∈Rd F ( w ) = 1 n n∑ i=1 fi ( w ) , ( 1 ) where w ∈ Rd denotes the model parameter , n denotes the number of training samples , fi ( w ) denotes the loss on the ith training sample . The problem in ( 1... | Large batch training has been observed to not only significantly improve the training speed but also lead to a worse generalization performance. This paper considers how to improve the performance of MSGD in large batch training. They propose the so called normalized MSGD where instead of the gradient, the algorithm us... | SP:276a1974451e9740ff761c45ff63de47aabe0534 |
Weighted Line Graph Convolutional Networks | 1 INTRODUCTION . Graph neural networks ( Gori et al. , 2005 ; Scarselli et al. , 2009 ; Hamilton et al. , 2017 ) have shown to be competent in solving challenging tasks in the field of network embedding . Many tasks have been significantly advanced by graph deep learning methods such as node classification tasks ( Kipf... | This paper introduces a weighted line graph formulation (WLGCL) which corrects the over-counting ("bias") of high-degree node features in a line-graph based convolutional network. Further, the paper uses Incidence Matrix to implement WLGCL updates which reduces the space complexity ($O(N^4) \to O(N^3)$) and time comple... | SP:c3236039988295311cdf505107bffa85b883e680 |
Weighted Line Graph Convolutional Networks | 1 INTRODUCTION . Graph neural networks ( Gori et al. , 2005 ; Scarselli et al. , 2009 ; Hamilton et al. , 2017 ) have shown to be competent in solving challenging tasks in the field of network embedding . Many tasks have been significantly advanced by graph deep learning methods such as node classification tasks ( Kipf... | The paper proposed a GNN model based on a weighted line graph, which adds weights to the line graph for the original input graph in a node/graph property prediction task. The line graph is a graph built on the original graph but with edges as nodes. A new convolution called weighted line graph convolution layer (WLGCL)... | SP:c3236039988295311cdf505107bffa85b883e680 |
Learning-Augmented Sketches for Hessians | Sketching is a dimensionality reduction technique where one compresses a matrix by often random linear combinations . A line of work has shown how to sketch the Hessian to speed up each iteration in a second order method , but such sketches usually depend only on the matrix at hand , and in a number of cases are even o... | The paper proposed a learned variant of the well-known iterative Hessian sketch (IHS) method of Pilanci and Wainwright, for efficiently solving least-squares regression. The proposed method is essentially a learned variant of the count-sketch, where the positions of the non-zero entries are random while the value is le... | SP:4647fc008073e5ee4e432f84e645aedb7faf736d |
Learning-Augmented Sketches for Hessians | Sketching is a dimensionality reduction technique where one compresses a matrix by often random linear combinations . A line of work has shown how to sketch the Hessian to speed up each iteration in a second order method , but such sketches usually depend only on the matrix at hand , and in a number of cases are even o... | Sketching is a popular technique in numerical linear algebra for achieving various desirable properties (e.g., lower complexity, one pass methods). The present paper considers a particular kind of sketch for which the sketch matrix is learned from data. It shows how such learned sketches can be used in two types of pro... | SP:4647fc008073e5ee4e432f84e645aedb7faf736d |
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream | 1 INTRODUCTION . Particular artificial neural networks ( ANNs ) are the leading mechanistic models of visual processing in the primate visual ventral stream ( Schrimpf et al. , 2020 ; Kubilius et al. , 2019 ; Dapello et al. , 2020 ) . After training on large-scale datasets such as ImageNet ( Deng et al. , 2009 ) by upd... | The paper is about ANN being best-known models of developed primate visual systems. However this fact does not yet mean that the way those systems are trained is also similar. This distinction and a step towards answering this question is the main motivation of this work. The authors demonstrate a set of ideas that whi... | SP:45d0d17b384044473db2e2e164c56558044d2542 |
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream | 1 INTRODUCTION . Particular artificial neural networks ( ANNs ) are the leading mechanistic models of visual processing in the primate visual ventral stream ( Schrimpf et al. , 2020 ; Kubilius et al. , 2019 ; Dapello et al. , 2020 ) . After training on large-scale datasets such as ImageNet ( Deng et al. , 2009 ) by upd... | This paper presents an empirical study that elucidates potential mechanisms through which models of adult-like visual streams can "develop" from less specific/coarser model instantiations. In particular, the authors consider existing ventral stream models whose internal representations and behavior are most brain-like ... | SP:45d0d17b384044473db2e2e164c56558044d2542 |
Stability analysis of SGD through the normalized loss function | 1 INTRODUCTION . In the last few years , deep learning has succeeded in establishing state-of-the-art performances in a wide variety of tasks in fields like computer vision , natural language processing and bioinformatics ( LeCun et al. , 2015 ) . Understanding when and how these networks generalize better is important... | This paper develops new stability bounds for SGD. The main difference from the existing studies is that they consider stability bounds for normalized loss functions where the parameters are normalized to have a norm of $1$. This paper considers both convex and nonconvex cases. For the convex case, the authors develop u... | SP:9070183afc9422af7dcef84aea785cb59bbba3ae |
Stability analysis of SGD through the normalized loss function | 1 INTRODUCTION . In the last few years , deep learning has succeeded in establishing state-of-the-art performances in a wide variety of tasks in fields like computer vision , natural language processing and bioinformatics ( LeCun et al. , 2015 ) . Understanding when and how these networks generalize better is important... | This paper considers the generalization bound for stochastic gradient descent. The authors leverage normalized loss function to analyze the stability of SGD algorithms which further yields the generalization bound. They provide the on-average stability result for non-convex optimization under the ReLU neural network se... | SP:9070183afc9422af7dcef84aea785cb59bbba3ae |
Learning Neural Generative Dynamics for Molecular Conformation Generation | 1 INTRODUCTION . Recently , we have witnessed the success of graph-based representations for molecular modeling in a variety of tasks such as property prediction ( Gilmer et al. , 2017 ) and molecule generation ( You et al. , 2018 ; Shi et al. , 2020 ) . However , a more natural and intrinsic representation of a molecu... | The authors of this manuscript proposed a generative dynamics system for the modelling and generation of 3D conformations of molecules. Specifically, there are three components: (1) conditional graph continuous flow (CGCF) to transform random noise to distances, (2)a closed-form distribution p(R|d, G), and (3) an ener... | SP:11a4f15893b32b9391d04a507bed8528a130f533 |
Learning Neural Generative Dynamics for Molecular Conformation Generation | 1 INTRODUCTION . Recently , we have witnessed the success of graph-based representations for molecular modeling in a variety of tasks such as property prediction ( Gilmer et al. , 2017 ) and molecule generation ( You et al. , 2018 ; Shi et al. , 2020 ) . However , a more natural and intrinsic representation of a molecu... | This paper presents an approach to generate diverse small molecule conformations given its graph by combining a conditional flow-based model with an energy-based model. Sampling is performed in two separate stages: 1) a normalizing flow produces a distribution over interatomic distances (which is then postprocessed int... | SP:11a4f15893b32b9391d04a507bed8528a130f533 |
MC-LSTM: Mass-conserving LSTM | 1 INTRODUCTION . Inductive biases enabled the success of CNNs and LSTMs . One of the greatest success stories of deep learning is Convolutional Neural Networks ( CNNs ) ( Fukushima , 1980 ; LeCun & Bengio , 1998 ; Schmidhuber , 2015 ; LeCun et al. , 2015 ) whose proficiency can be attributed to their strong inductive b... | In this paper the authors propose a novel architecture, called Mass-Conserving LSTM (MC-LSTM) based on LSTM. The authors base their work over the hypothesis that the real world is based over conservation laws related to mass, energy, etc. Thus, they propose that also the quantities involved in deep learning models shou... | SP:fdd497d17b5a12017b4ceb377de57bfc18ebd815 |
MC-LSTM: Mass-conserving LSTM | 1 INTRODUCTION . Inductive biases enabled the success of CNNs and LSTMs . One of the greatest success stories of deep learning is Convolutional Neural Networks ( CNNs ) ( Fukushima , 1980 ; LeCun & Bengio , 1998 ; Schmidhuber , 2015 ; LeCun et al. , 2015 ) whose proficiency can be attributed to their strong inductive b... | The paper provides an interesting and novel LSTM structure named MC-LSTM, which extends the inductive bias of LSTM to deal with some real-world problems limited by conservation laws. The authors do some experiments related to traffic forecasting and hydrology to illustrate the effectiveness of MC-LSTM. The new archite... | SP:fdd497d17b5a12017b4ceb377de57bfc18ebd815 |
Apollo: An Adaptive Parameter-wised Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization | 1 INTRODUCTION . Nonconvex stochastic optimization is of core practical importance in many fields of machine learning , in particular for training deep neural networks ( DNNs ) . First-order gradient-based optimization algorithms , conceptually attractive due to their linear efficiency on both the time and memory compl... | The paper proposes a Quasi-Newton inspired optimization algorithm for Stochastic Optimization named APOLLO. It adjusts a previously known update formula to better suit Deep Learning by using 1) a layer-wise diagonal approximation to the Hessian, 2) an exponential average of gradients to address the noise. Overall the a... | SP:69cc1499e1ffdff113346180dd31c60fb1059872 |
Apollo: An Adaptive Parameter-wised Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization | 1 INTRODUCTION . Nonconvex stochastic optimization is of core practical importance in many fields of machine learning , in particular for training deep neural networks ( DNNs ) . First-order gradient-based optimization algorithms , conceptually attractive due to their linear efficiency on both the time and memory compl... | This paper presents the optimization method Apollo, a quasi-Newton method that relies on a parameter-wise version of the weak secant condition to allow for a diagonal approximation of the Hessian. Additionally, the issue of a potentially non-PSD approximation is addressed by replacing the approximation with a rectified... | SP:69cc1499e1ffdff113346180dd31c60fb1059872 |
Benchmarking Unsupervised Object Representations for Video Sequences | 1 INTRODUCTION . Humans understand the world in terms of objects . Being able to decompose our environment into independent objects that can interact with each other is an important prerequisite for reasoning and scene understanding . Similarly , an artificial intelligence system would benefit from the ability to both ... | The paper proposes a benchmark for the evaluation of unsupervised learning of object-centric representation. The benchmark consists of three datasets, multi-object tracking metrics and of the evaluation of four methods. The proposed dataset consists of three sets of video sequences, procedurally generated, which are ei... | SP:9e9ae7233f8037f5ae0ef4b641027dd46b997324 |
Benchmarking Unsupervised Object Representations for Video Sequences | 1 INTRODUCTION . Humans understand the world in terms of objects . Being able to decompose our environment into independent objects that can interact with each other is an important prerequisite for reasoning and scene understanding . Similarly , an artificial intelligence system would benefit from the ability to both ... | The paper presents an empirical evaluation of a number of recent models for unsupervised object-based video modelling. Five different models are evaluated on three (partially novel) benchmarks, providing a unifying perspective on the relative performance of these models. Several common issues are identified and highlig... | SP:9e9ae7233f8037f5ae0ef4b641027dd46b997324 |
Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions | 1 INTRODUCTION . Stochastic optimization has found tremendous applications in training highly expressive machine learning models including deep neural networks ( DNNs ) ( Bottou et al. , 2018 ) , which are ubiquitous in modern learning architectures ( LeCun et al. , 2015 ) . Oftentimes , the models trained in this way ... | This paper studies the generalization performance of stochastic algorithms in nonconvex optimization with gradient dominance condition. In detail, the authors suggest that for any algorithm, its generalization error can be bounded by $O(1/(n\beta))$ plus the optimization error of the algorithm, where $\beta$ is the gra... | SP:8cbce41127c32edb148b2d6713f4ecec0efc6ff9 |
Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions | 1 INTRODUCTION . Stochastic optimization has found tremendous applications in training highly expressive machine learning models including deep neural networks ( DNNs ) ( Bottou et al. , 2018 ) , which are ubiquitous in modern learning architectures ( LeCun et al. , 2015 ) . Oftentimes , the models trained in this way ... | This paper mainly studies the generalization performance of stochastic algorithms. Compared with previous results which rely on Lipschitz condition, this paper assumes smoothness condition and Polyak-Lojasiewicz Condition, and then prove the excess generalization bound that is a summation of $\frac{1}{n\beta}$ and empi... | SP:8cbce41127c32edb148b2d6713f4ecec0efc6ff9 |
BASGD: Buffered Asynchronous SGD for Byzantine Learning | 1 INTRODUCTION . Due to the wide application in cluster-based large-scale learning , federated learning ( Konevcnỳ et al. , 2016 ; Kairouz et al. , 2019 ) , edge computing ( Shi et al. , 2016 ) and so on , distributed learning has recently become a hot research topic ( Zinkevich et al. , 2010 ; Yang , 2013 ; Jaggi et ... | The paper proposes a practical asynchronous stochastic gradient descent for Byzantine distributed learning where some of transmitted gradients are likely to be replaced by arbitrary vectors. Specifically, the server temporarily stores gradients on multiple (namely $B$) buffers and performs a proper robust aggregation ... | SP:28b164b471496b8f4c07128fa107df88a9dac3e9 |
BASGD: Buffered Asynchronous SGD for Byzantine Learning | 1 INTRODUCTION . Due to the wide application in cluster-based large-scale learning , federated learning ( Konevcnỳ et al. , 2016 ; Kairouz et al. , 2019 ) , edge computing ( Shi et al. , 2016 ) and so on , distributed learning has recently become a hot research topic ( Zinkevich et al. , 2010 ; Yang , 2013 ; Jaggi et ... | Review: This paper proposes BASGD which uses buffers to perform asynchronous Byzantine learning. In each SGD step, all workers compute gradients and send them to the server where their ad buffer is updated. When all of the buffers are updated, the server performs an model update. When a worker send a gradient to the se... | SP:28b164b471496b8f4c07128fa107df88a9dac3e9 |
What Preserves the Emergence of Language? | 1 INTRODUCTION . Unveiling the principles behind the emergence and evolution of language is attractive and appealing to all . It is believed that this research field is of great significance for promoting the development of enabling agents to evolve an efficient communication protocol ( Nowak & Krakauer , 1999 ; Kottur... | This paper investigates conditions under which communities of cooperative agents are stable. Communities in multi-round bargaining games with evolutionary dynamics are evaluated in three main setups. The first imposes no restrictions on the agents' behavior and is shown to be easily invaded by deceitful agents. The sec... | SP:e898ffa6bfdc1597ced0f9bd66c60ff9c6b4c383 |
What Preserves the Emergence of Language? | 1 INTRODUCTION . Unveiling the principles behind the emergence and evolution of language is attractive and appealing to all . It is believed that this research field is of great significance for promoting the development of enabling agents to evolve an efficient communication protocol ( Nowak & Krakauer , 1999 ; Kottur... | This paper attempts to address a question in the emergent communication literature: what preserves / maintains the stability of emerged communication protocols. The authors manipulate the prevalence of lying behavior in a community of agents playing a variant of a Nash bargaining game. The main take-away is that expl... | SP:e898ffa6bfdc1597ced0f9bd66c60ff9c6b4c383 |
Towards Impartial Multi-task Learning | 1 INTRODUCTION . Recent deep networks in computer vision can match or even surpass human beings on some specific tasks separately . However , in reality multiple tasks ( e.g. , semantic segmentation and depth estimation ) must be solved simultaneously . Multi-task learning ( MTL ) ( Caruana , 1997 ; Evgeniou & Pontil ,... | This paper presents a satisfying solution to the open problem of how to train all tasks at approximately the same rate in multi-task learning. There has been a bunch of work on this problem in the last few years. This paper characterizes existing work w.r.t. the fairness of training across tasks in order to motivate tw... | SP:c3bdf7ffa026668d98d241b72ee14e2a3510a7d9 |
Towards Impartial Multi-task Learning | 1 INTRODUCTION . Recent deep networks in computer vision can match or even surpass human beings on some specific tasks separately . However , in reality multiple tasks ( e.g. , semantic segmentation and depth estimation ) must be solved simultaneously . Multi-task learning ( MTL ) ( Caruana , 1997 ; Evgeniou & Pontil ,... | The authors propose to balance multi-task training using IMTL-G on the shared backbone and IMTL-L on the task-specific branches. IMTL-G enforces equal gradient projections between tasks with a close-form formulation to calculate the desired gradient weightings $\alpha$. IMTL-L learns the loss weightings $e^s$ with a re... | SP:c3bdf7ffa026668d98d241b72ee14e2a3510a7d9 |
Uncertainty-aware Active Learning for Optimal Bayesian Classifier | For pool-based active learning , in each iteration a candidate training sample is chosen for labeling by optimizing an acquisition function . In Bayesian classification , expected Loss Reduction ( ELR ) methods maximize the expected reduction in the classification error given a new labeled candidate based on a one-step... | This paper studies the label solicitation strategy in active learning. In particular, it focuses on the expected loss reduction (ELR) strategy, analyzes its problem, and modifies the original ELR method to make sure the active learner converges to the optimal classifier along learning iterations. The paper provides the... | SP:839f449191ae3ff1016d4321d9e1926c5f883a78 |
Uncertainty-aware Active Learning for Optimal Bayesian Classifier | For pool-based active learning , in each iteration a candidate training sample is chosen for labeling by optimizing an acquisition function . In Bayesian classification , expected Loss Reduction ( ELR ) methods maximize the expected reduction in the classification error given a new labeled candidate based on a one-step... | This paper provides an interesting algorithm to address the previous Bayesian active learning query strategy in (binary) classification. By the simple modification, the algorithm can overcome the drawbacks of ELR in the convergence to the optimal classifier parameterized by $\theta_r$. In experiments, the proposed algo... | SP:839f449191ae3ff1016d4321d9e1926c5f883a78 |
Exploring Balanced Feature Spaces for Representation Learning | Existing self-supervised learning ( SSL ) methods are mostly applied for training representation models from artificially balanced datasets ( e.g . ImageNet ) . It is unclear how well they will perform in the practical scenarios where datasets are often imbalanced w.r.t . the classes . Motivated by this question , we c... | **Overview:** The paper presents experiments showing that the contrastive learning losses produce better embeddings or feature spaces than those produced by using binary cross-entropy losses. The experiments show that embeddings learned using contrastive learning losses seem to favor long-tailed learning tasks, out-of-... | SP:6c897187759edf48c1bd4f3536c098ac0d5f1179 |
Exploring Balanced Feature Spaces for Representation Learning | Existing self-supervised learning ( SSL ) methods are mostly applied for training representation models from artificially balanced datasets ( e.g . ImageNet ) . It is unclear how well they will perform in the practical scenarios where datasets are often imbalanced w.r.t . the classes . Motivated by this question , we c... | In this paper, the authors propose a new loss function to learn feature representations for image datasets that are class-imbalanced. The loss function is a simple yet effective tweak on an existing supervised contrastive loss work. A number of empirical tests are performed on long-tailed datasets showing the benefits ... | SP:6c897187759edf48c1bd4f3536c098ac0d5f1179 |
Contrastive Learning with Adversarial Perturbations for Conditional Text Generation | 1 INTRODUCTION . The sequence-to-sequence ( seq2seq ) models ( Sutskever et al. , 2014 ) , which learn to map an arbitrary-length input sequence to another arbitrary-length output sequence , have successfully tackled a wide range of language generation tasks . Early seq2seq models have used recurrent neural networks to... | Proposes contrastive learning method for conditional text-generation. Here we maximize similarity (of representations) between source and target sequences (positive) while minimizing similarity with false targets (negative). Additional positives and negatives are created in the sequence representation space by adding p... | SP:978b2e085614592b4d8503ea2cc17ff5f0510539 |
Contrastive Learning with Adversarial Perturbations for Conditional Text Generation | 1 INTRODUCTION . The sequence-to-sequence ( seq2seq ) models ( Sutskever et al. , 2014 ) , which learn to map an arbitrary-length input sequence to another arbitrary-length output sequence , have successfully tackled a wide range of language generation tasks . Early seq2seq models have used recurrent neural networks to... | This paper proposes to add contrastive learning to the sequence-to-sequence generation problem. More specifically, the authors apply a contrastive loss on the globally pooled hidden representation of the generated hidden states. The key novelty is to apply adversarial gradients to obtain both hard negative and hard pos... | SP:978b2e085614592b4d8503ea2cc17ff5f0510539 |
Adversarially Robust Federated Learning for Neural Networks | In federated learning , data is distributed among local clients which collaboratively train a prediction model using secure aggregation . To preserve the privacy of the clients , the federated learning paradigm requires each client to maintain a private local training data set , and only uploads its summarized model up... | The paper studies adversarial robustness in the context of federated learning. The authors provide an algorithm for adversarial training that generates adversarial examples on a trusted public dataset and iteratively sends them to the clients, so that they can perform learning on the adversarial examples as well. Notab... | SP:385bf55e0a9bdb8a3f3db800f63acffcb4207927 |
Adversarially Robust Federated Learning for Neural Networks | In federated learning , data is distributed among local clients which collaboratively train a prediction model using secure aggregation . To preserve the privacy of the clients , the federated learning paradigm requires each client to maintain a private local training data set , and only uploads its summarized model up... | The authors propose a robust federated learning algorithm, where they assume that all samples are iid, and $n_s$ clean samples are available at the server side. The authors then go on to optimize a loss function that optimizes the aggregate loss and propose some new algorithms with experimental results. While overall t... | SP:385bf55e0a9bdb8a3f3db800f63acffcb4207927 |
Towards Data Distillation for End-to-end Spoken Conversational Question Answering | 1 INTRODUCTION . Conversational Machine Reading Comprehension ( CMRC ) has been studied extensively over the past few years within the natural language processing ( NLP ) communities ( Zhu et al. , 2018 ; Liu et al. , 2019 ; Yang et al. , 2019 ) . Different from traditional MRC tasks , CMRC aims to enable models to lea... | This paper proposes a new task: spoken conversational question answering, which combines conversational question answering (e.g. CoQA) with spoken question answering (e.g. Spoken-SQuAD). The task is to answer a question (in written text) given a question that is given in both audio form and text form. They create a dat... | SP:06c25da862ae69fa7cd0f87ea0b125243ea86f5f |
Towards Data Distillation for End-to-end Spoken Conversational Question Answering | 1 INTRODUCTION . Conversational Machine Reading Comprehension ( CMRC ) has been studied extensively over the past few years within the natural language processing ( NLP ) communities ( Zhu et al. , 2018 ; Liu et al. , 2019 ; Yang et al. , 2019 ) . Different from traditional MRC tasks , CMRC aims to enable models to lea... | In this paper, the authors release a new dataset - Spoken-CoQA which includes an ASR based version of the popular CoQA dataset. The dataset has been created by running the Google TTS system followed by ASR using CMU Sphinx, to create a speech-transcribed versions of the dataset. The dataset includes the corresponding ... | SP:06c25da862ae69fa7cd0f87ea0b125243ea86f5f |
Personalized Federated Learning with First Order Model Optimization | 1 INTRODUCTION . Federated learning ( FL ) has shown great promise in recent years for training a single global model over decentralized data . While seminally motivated by effective inference on a general test set similar in distribution to the decentralized data in aggregate ( McMahan et al. , 2016 ; Bonawitz et al. ... | **Paper Summary:** The paper addresses an important topic in federated learning which is personalization. The authors propose a two steps process to achieve the personalization: 1. Figuring out which models to send to which clients; 2. Computing their personalized weighted combinations for each client. To determine the... | SP:f55167c38de1d6b8528b2d4ef865f5e2e87a5bdc |
Personalized Federated Learning with First Order Model Optimization | 1 INTRODUCTION . Federated learning ( FL ) has shown great promise in recent years for training a single global model over decentralized data . While seminally motivated by effective inference on a general test set similar in distribution to the decentralized data in aggregate ( McMahan et al. , 2016 ; Bonawitz et al. ... | The paper proposes a new FL method that computes in every communication round for each client a personalized model as starting point for the next round of federation. The paper defines the client-specific objective as some loss function of the weighted combination of all (or subset) models on a client-specific validati... | SP:f55167c38de1d6b8528b2d4ef865f5e2e87a5bdc |
Tilted Empirical Risk Minimization | 1 INTRODUCTION . Many statistical estimation procedures rely on the concept of empirical risk minimization ( ERM ) , in which the parameter of interest , θPΘĎRd , is estimated by minimizing an average loss over the data : Rpθq : “ 1 N ÿ iPrNs fpxi ; θq . ( 1 ) While ERM is widely used and has nice statistical propertie... | This work analyzes the LogSumExp aggregated loss (named tiled empirical risk minimization, or TERM, in the paper). It provides several general properties of the loss, such as its relation to min/avg/max-loss, and interpretations of different trade-offs. Empirically, it is shown that TERM can be applied to a diverse set... | SP:478a18897696ba946947faeee860203186d7e756 |
Tilted Empirical Risk Minimization | 1 INTRODUCTION . Many statistical estimation procedures rely on the concept of empirical risk minimization ( ERM ) , in which the parameter of interest , θPΘĎRd , is estimated by minimizing an average loss over the data : Rpθq : “ 1 N ÿ iPrNs fpxi ; θq . ( 1 ) While ERM is widely used and has nice statistical propertie... | This paper considers a unified framework named TERM for addressing a bunch of problems arising in the simple averaged empirical minimization. By leveraging the key hyper-parameter t in the TERM loss, it can recover the original average loss and approximate robust loss, min/max loss, and the superquantile loss, etc. The... | SP:478a18897696ba946947faeee860203186d7e756 |
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients | 1 INTRODUCTION . Mobile devices and the Internet of Things ( IoT ) devices are becoming the primary computing resource for billions of users worldwide ( Lim et al. , 2020 ) . These devices generate a significant amount of data that can be used to improve numerous existing applications ( Hard et al. , 2018 ) . From the ... | This work presents a novel FL algorithm named HeteroFL (the name might sounds weird to some peoples) and 3 different simple methods to improve FL in heterogeneous conditions (i.e. both in term of clients and data partitioning). These tricks are: 1. A revised batchnormalisation; 2. a pre-activity scaling; 3. a masked lo... | SP:56e4d560f80360bd6f50d162caade651b5ff91a6 |
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients | 1 INTRODUCTION . Mobile devices and the Internet of Things ( IoT ) devices are becoming the primary computing resource for billions of users worldwide ( Lim et al. , 2020 ) . These devices generate a significant amount of data that can be used to improve numerous existing applications ( Hard et al. , 2018 ) . From the ... | This paper proposes a new federated learning framework called HeteroFL, which supports the training of different sizes of local models in heterogeneous clients. Clients with higher computation capability can train larger models while clients with less computation capability train smaller models, and all these model arc... | SP:56e4d560f80360bd6f50d162caade651b5ff91a6 |
AlgebraNets | 1 Introduction . Nearly universally , the atomic building blocks of artificial neural networks are scalar real-valued weights and scalar real-valued neuron activations that interact using standard rules of multiplication and addition . We propose AlgebraNets , where we replace the commonly used real-valued algebra with... | In this paper, the authors propose the usage of complex numbers in deep neural networks. Would be good to know that complex numbers, n x n matrices, quaternions, diagonal matrices, etc. all can be used in neural networks. The authors also claims benchmark performance in large-scale image classification and language mod... | SP:9fad18ae03570219f7b9fd631dc6eccbbb41fa30 |
AlgebraNets | 1 Introduction . Nearly universally , the atomic building blocks of artificial neural networks are scalar real-valued weights and scalar real-valued neuron activations that interact using standard rules of multiplication and addition . We propose AlgebraNets , where we replace the commonly used real-valued algebra with... | The authors propose AlgebraNets - a previously explored approach to replace real-valued algebra in deep learning models with other associative algebras that include 2x2 matrices over real and complex numbers. They provide a comprehensive overview of prior methods in this direction and motivate their work with potential... | SP:9fad18ae03570219f7b9fd631dc6eccbbb41fa30 |
Graph Convolution with Low-rank Learnable Local Filters | 1 Introduction . Deep methods have achieved great success in visual cognition , yet they still lack capability to tackle severe geometric transformations such as rotation , scaling and viewpoint changes . This problem is often handled by conducting data augmentations with these geometric variations included , e.g . by ... | This paper proposed L3Net which is a new graph convolution with decomposing the learnable local filters into low-rank. It can contain both spatial and spectral graph convolution (including ChebNet, GAT, EdgeNet and so on) as subsets. It is also robust to graph noise. Experiments are conducted on mesh data, facial recog... | SP:cbfb4439fcbf27dc2c05675123b7b0555acdbf33 |
Graph Convolution with Low-rank Learnable Local Filters | 1 Introduction . Deep methods have achieved great success in visual cognition , yet they still lack capability to tackle severe geometric transformations such as rotation , scaling and viewpoint changes . This problem is often handled by conducting data augmentations with these geometric variations included , e.g . by ... | The paper presents a graph neural network (GNN) architecture with learnable low-rank filters that unifies various recently-proposed GNN-based methods. The local filters substitute the graph shift operator (GSO) by a learnable set of parameters that capture the local connectivity of each node in the graph. Moreover, a r... | SP:cbfb4439fcbf27dc2c05675123b7b0555acdbf33 |
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs | 1 INTRODUCTION . Reasoning , a process of inferring new knowledge from available facts , has long been considered an essential topic in AI research . Recently , reasoning on knowledge graphs ( KG ) has gained increasing interest ( Das et al. , 2017 ; Ren et al. , 2020 ; Hildebrandt et al. , 2020 ) . A knowledge graph i... | This paper proposes xERTE, a comprehensive set of strategies (i.e. a temporal relational attention mechanism and a human-mimic representation update scheme, temporal neighborhood sampling and pruning, etc.) for link forecasting in temporal knowledge graphs (tKGs). Experiments on real-world tKGs show significant improve... | SP:7a333ae10f9732f3e0bed9bf009914e5d1bc265f |
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs | 1 INTRODUCTION . Reasoning , a process of inferring new knowledge from available facts , has long been considered an essential topic in AI research . Recently , reasoning on knowledge graphs ( KG ) has gained increasing interest ( Das et al. , 2017 ; Ren et al. , 2020 ; Hildebrandt et al. , 2020 ) . A knowledge graph i... | Authors have presented a method to forecast future links on temporal knowledge graphs (KGs). They use attention mechanisms to extract a query-dependent subgraph. According to the authors, this extracted subgraph provides a graphical explanation of the prediction. Authors have performed an ablation study to denote the e... | SP:7a333ae10f9732f3e0bed9bf009914e5d1bc265f |
Federated Learning with Decoupled Probabilistic-Weighted Gradient Aggregation | 1 INTRODUCTION . Federated learning ( FL ) has emerged as a novel distributed machine learning paradigm that allows a global machine learning model to be trained by multiple mobile clients collaboratively . In such paradigm , mobile clients train local models based on datasets generated by edge devices such as sensors ... | The paper proposed FedDEC, a novel approach to conduct model updates aggregation in federated learning. The main motivation of this paper is to decouple the aggregation of normal model weights and statistics in BNs separately such that both data and model heterogeneity can be handled. Theoretical analysis indicates tha... | SP:759c0a0298f9845f41d6b556a2187867230a0ca5 |
Federated Learning with Decoupled Probabilistic-Weighted Gradient Aggregation | 1 INTRODUCTION . Federated learning ( FL ) has emerged as a novel distributed machine learning paradigm that allows a global machine learning model to be trained by multiple mobile clients collaboratively . In such paradigm , mobile clients train local models based on datasets generated by edge devices such as sensors ... | This paper introduces an aggregation mechanism designed for neural networks with batch normalisation layers. This mechanism relies on two parts: probabilistic mixing weights of the loss function and the use of a weighted pool estimator for aggregating the BN variance parameters. The mixing weights are derived from a GM... | SP:759c0a0298f9845f41d6b556a2187867230a0ca5 |
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks | 1 INTRODUCTION . Humans extrapolate well in many tasks . For example , we can apply arithmetics to arbitrarily large numbers . One may wonder whether a neural network can do the same and generalize to examples arbitrarily far from the training data ( Lake et al. , 2017 ) . Curiously , previous works report mixed extrap... | This paper tackles the challenging question of how deep networks might learn to extrapolate knowledge outside the support of their training distribution. The paper contributes both with novel theoretical arguments as well as with empirical evidence collected on targeted cases. Differently from other recent approaches t... | SP:43728b5763907cbe84f1c7ded63e5f63c45415c5 |
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks | 1 INTRODUCTION . Humans extrapolate well in many tasks . For example , we can apply arithmetics to arbitrarily large numbers . One may wonder whether a neural network can do the same and generalize to examples arbitrarily far from the training data ( Lake et al. , 2017 ) . Curiously , previous works report mixed extrap... | This paper analyzes the extrapolate ability of MLPs and GNNs. In contrast to the existing theoretical works that focus on generalizability and capacity of these models, this paper emphasizes the behavior of training algorithm using gradient descent. It takes analogy of kernel regression via the neural tangent kernel a... | SP:43728b5763907cbe84f1c7ded63e5f63c45415c5 |
STRATA: Simple, Gradient-free Attacks for Models of Code | 1 INTRODUCTION . Although machine learning has been shown to be effective at a wide variety of tasks across computing , statistical models are susceptible to adversarial examples . Adversarial examples , first identified in the continuous domain by Szegedy et al . ( 2014 ) , are imperceptible perturbations to input tha... | This paper proposes STRATA, a simple adversarial attack against the code2seq model. The key idea is to replace local variable names in the input code with other randomly chosen sub-tokens with embedding vectors of relatively high L2 norms. Meanwhile, they observe that such tokens often appear frequently in the training... | SP:8c168e9fb22c78e446487b4c0c4b3a1e27a716aa |
STRATA: Simple, Gradient-free Attacks for Models of Code | 1 INTRODUCTION . Although machine learning has been shown to be effective at a wide variety of tasks across computing , statistical models are susceptible to adversarial examples . Adversarial examples , first identified in the continuous domain by Szegedy et al . ( 2014 ) , are imperceptible perturbations to input tha... | This paper proposes STRATA, a novel adversarial attack against source code models, more precisely against code2seq. The attack strategy can be applied under black- or white-box threat models, targeted or untargeted. Adversarial training is based on STRATA adversarial examples is proposed to render the models robust. Ex... | SP:8c168e9fb22c78e446487b4c0c4b3a1e27a716aa |
Deep Ensembles for Low-Data Transfer Learning | 1 INTRODUCTION . There are many ways to construct models with minimal data . It has been shown that fine-tuning pre-trained deep models is a compellingly simple and performant approach ( Dhillon et al. , 2020 ; Kolesnikov et al. , 2019 ) , and this is the paradigm our work operates in . It is common to use networks pre... | Paper proposed an ensemble learning approach for the low-data regime. Paper uses various sources of diversity - pre-training, fine-tuning and combined to create ensembles. It then uses nearest-neighbor accuracy to rank pre-trained models, fine-tune the best ones with a small hyper-parameter sweep, and greedily construc... | SP:9a4c3ea3b70f57c94a649f12b8c85c35e6b3b189 |
Deep Ensembles for Low-Data Transfer Learning | 1 INTRODUCTION . There are many ways to construct models with minimal data . It has been shown that fine-tuning pre-trained deep models is a compellingly simple and performant approach ( Dhillon et al. , 2020 ; Kolesnikov et al. , 2019 ) , and this is the paradigm our work operates in . It is common to use networks pre... | [Summary] This paper presents different ways of creating ensembles from pre-trained models. Specifically, authors first utilize nearest-neighbor accuracy to to rank pre-trained models, then fine-tune the best ones with a small hyperparameter sweep, and finally greedily construct an ensemble to minimize validation cross... | SP:9a4c3ea3b70f57c94a649f12b8c85c35e6b3b189 |
Transferable Recognition-Aware Image Processing | 1 INTRODUCTION Unlike in image recognition where a neural network maps an image to a semantic label , a neural network used for image processing maps an input image to an output image with some desired properties . Examples include image super-resolution ( Dong et al. , 2014 ) , denoising ( Xie et al. , 2012 ) , deblur... | This paper proposes a setting called "recognition-aware image processing." The key idea is to make the images output by image processing methods still be readily recognized by image recognition methods. Realizing this will help to better meet the requirement from both human observers and machines. Formally, this is for... | SP:f17ad6d00a23e46ebe9175e1eeea7d3eef7f8c84 |
Transferable Recognition-Aware Image Processing | 1 INTRODUCTION Unlike in image recognition where a neural network maps an image to a semantic label , a neural network used for image processing maps an input image to an output image with some desired properties . Examples include image super-resolution ( Dong et al. , 2014 ) , denoising ( Xie et al. , 2012 ) , deblur... | The paper proposed a learnable image processing methods that improve machine interpretability of processed image. The paper mainly claimed that improvement of machine recognition is transferrable when evaluated on models of different architectures, recognized categories, tasks and training datasets. Additionally, the p... | SP:f17ad6d00a23e46ebe9175e1eeea7d3eef7f8c84 |
Least Probable Disagreement Region for Active Learning | 1 INTRODUCTION . Active learning ( Cohn et al. , 1996 ) is a subfield of machine learning to attain data efficiency with fewer labeled training data when it is allowed to choose the training data from which to learn . For many real-world learning problems , large collections of unlabeled samples is assumed available , ... | The paper defines a new measure of distance between a hypothesis $h$ and a point $x$, which is the probability mass of the smallest (by probability mass) disagreement region (induced by the other $h' \in \mathcal{H}$) containing $x$. In general this is intractable so the authors offer two assumptions about the relation... | SP:213a295549ebc49eda533baf77de2e0aed81cbb1 |
Least Probable Disagreement Region for Active Learning | 1 INTRODUCTION . Active learning ( Cohn et al. , 1996 ) is a subfield of machine learning to attain data efficiency with fewer labeled training data when it is allowed to choose the training data from which to learn . For many real-world learning problems , large collections of unlabeled samples is assumed available , ... | This paper is motivated by the idea that unlabelled samples near the estimated decision boundary show to be very informative/useful in an active learning setting. However, measuring the distance between an instance and the decision boundary is a non-trivial task in numerous machine learning algorithms, especially in de... | SP:213a295549ebc49eda533baf77de2e0aed81cbb1 |
Contrastive Divergence Learning is a Time Reversal Adversarial Game | 1 INTRODUCTION . Unnormalized probability models have drawn significant attention over the years . These models arise , for example , in energy based models , where the normalization constant is intractable to compute , and are thus relevant to numerous settings . Particularly , they have been extensively used in the c... | This paper presents a way to view contrastive divergence (CD) learning as an adversarial learning procedure where a discriminator is tasked with classifying whether or not a Markov chain, generated from the model, has been time-reversed. Beginning with the classic derivation of CD and its approximate gradient, noting r... | SP:14fa0894cc0b4dd4bdb51c089cf5511c89de8b4f |
Contrastive Divergence Learning is a Time Reversal Adversarial Game | 1 INTRODUCTION . Unnormalized probability models have drawn significant attention over the years . These models arise , for example , in energy based models , where the normalization constant is intractable to compute , and are thus relevant to numerous settings . Particularly , they have been extensively used in the c... | To implement the contrastive divergence (CD) algorithm in practice, an intractable term is typically omitted from the gradient. This leads to an approximation. This work shows that the resulting algorithm can in fact be viewed as an exact algorithm targeting a different, adversarial objective. The derivation in this pa... | SP:14fa0894cc0b4dd4bdb51c089cf5511c89de8b4f |
Conditional Networks | 1 INTRODUCTION . Deep learning has achieved great success in many core artificial intelligence ( AI ) tasks ( Hinton et al. , 2012 ; Krizhevsky et al. , 2012 ; Brown et al. , 2020 ) over the past decade . This is often attributed to better computational resources ( Brock et al. , 2018 ) and large-scale datasets ( Deng ... | This submission proposes an approach to modulate activations of general convolutional neural networks by means of an auxiliary network trained on additional metadata to a dataset. The specific goal is to improve out-of-distribution (OOD) generalisation. This *conditional network* approach is illustrated for two standar... | SP:beaf78b9053a49c23e984589327f48513f1d4277 |
Conditional Networks | 1 INTRODUCTION . Deep learning has achieved great success in many core artificial intelligence ( AI ) tasks ( Hinton et al. , 2012 ; Krizhevsky et al. , 2012 ; Brown et al. , 2020 ) over the past decade . This is often attributed to better computational resources ( Brock et al. , 2018 ) and large-scale datasets ( Deng ... | This paper aims to tackle the out-of-distribution generalization problem where a model needs to generalize to new distributions at test time. The authors propose to utilize some extra information like the additional annotations as the conditional input and output the affine transformation parameters for the batch norma... | SP:beaf78b9053a49c23e984589327f48513f1d4277 |
Sself: Robust Federated Learning against Stragglers and Adversaries | 1 INTRODUCTION . Large volumes of data collected at various edge devices ( i.e. , smart phones ) are valuable resources in training machine learning models with a good accuracy . Federated learning ( McMahan et al. , 2017 ; Li et al. , 2019a ; b ; Konečnỳ et al. , 2016 ) is a promising direction for large-scale learn... | This paper considers federated learning with straggling and adversarial devices. To tackle stragglers, the paper proposes semi-synchronous averaging wherein models with the same staleness are first averaged together, and then a weighted average of the results with different stateless is computed. To mitigate adversarie... | SP:03a7c25f464f8e293bf300d897342f5f82a51f28 |
Sself: Robust Federated Learning against Stragglers and Adversaries | 1 INTRODUCTION . Large volumes of data collected at various edge devices ( i.e. , smart phones ) are valuable resources in training machine learning models with a good accuracy . Federated learning ( McMahan et al. , 2017 ; Li et al. , 2019a ; b ; Konečnỳ et al. , 2016 ) is a promising direction for large-scale learn... | The paper claims to propose the first algorithm that can handle adversarial machines and stragglers simultaneously in the federated learning setting. To handle stragglers, the paper takes a semi-synchronous approach by taking a weighted sum of gradients depending on staleness. To handle adversarial machines, the algori... | SP:03a7c25f464f8e293bf300d897342f5f82a51f28 |
Run Away From your Teacher: a New Self-Supervised Approach Solving the Puzzle of BYOL | 1 INTRODUCTION . Recently the performance gap between self-supervised learning and supervised learning has been narrowed thanks to the development of contrastive learning ( Chen et al. , 2020b ; a ; Tian et al. , 2019 ; Chen et al. , 2020b ; Sohn , 2016 ; Zhuang et al. , 2019 ; He et al. , 2020 ; Oord et al. , 2018 ; H... | the paper aims to explain the success of BYOL, a recently proposed contrastive method that mysteriously avoids the trivial constant solution without requiring negative samples. The paper proposes a new loss named RAFT. Compared to BYOL, RAFT is more general since it subsumes a variation of BYOL as its special case, and... | SP:a27d66876fcdc3f3871485445e09041a8927b147 |
Run Away From your Teacher: a New Self-Supervised Approach Solving the Puzzle of BYOL | 1 INTRODUCTION . Recently the performance gap between self-supervised learning and supervised learning has been narrowed thanks to the development of contrastive learning ( Chen et al. , 2020b ; a ; Tian et al. , 2019 ; Chen et al. , 2020b ; Sohn , 2016 ; Zhuang et al. , 2019 ; He et al. , 2020 ; Oord et al. , 2018 ; H... | The paper provides a new perspective on the BYOL self-supervised learning method. First, the paper introduces an upper-bound objective, BYOL', that is easier to analyze than BYOL because it is composed of two well understood losses: an alignment loss and cross-model loss. Further, it shows empirically that optimizing B... | SP:a27d66876fcdc3f3871485445e09041a8927b147 |
With False Friends Like These, Who Can Have Self-Knowledge? | 1 INTRODUCTION . Deep neural networks ( DNNs ) have achieved breakthroughs in a variety of challenging problems such as image understanding ( Krizhevsky et al. , 2012 ) , speech recognition ( Graves et al. , 2013 ) , and automatic game playing ( Mnih et al. , 2015 ) . Despite these remarkable successes , their pervasiv... | 1. The premise of the paper is that the adversary can perturb the *test* set so that the model is shown to perform better that it really is capable of. And in Section 7 (Conclusion) the paper claims that it exposes this new risk. However, remember that this risk is already mitigated in practice by keeping the test data... | SP:0af1989b2e643d013174489704d0a052bad77f95 |
With False Friends Like These, Who Can Have Self-Knowledge? | 1 INTRODUCTION . Deep neural networks ( DNNs ) have achieved breakthroughs in a variety of challenging problems such as image understanding ( Krizhevsky et al. , 2012 ) , speech recognition ( Graves et al. , 2013 ) , and automatic game playing ( Mnih et al. , 2015 ) . Despite these remarkable successes , their pervasiv... | This paper presents a new kind of adversarial attacks, named hypocritical attack. It is a reverse version of the original adversarial attack. It tricks a model into classifying data correctly with a perturbation. This can be a problem since it can make people satisfy the model performance, but the model is not robust o... | SP:0af1989b2e643d013174489704d0a052bad77f95 |
Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures | 1 INTRODUCTION . Clustering is an unsupervised machine learning problem , where the task is to group objects that are similar to each other . In network analysis , a related problem is called community detection , where grouping is based on relations between items ( links ) , and the obtained clusters are expected to b... | This paper aims to answer a very important and difficult question, i.e., given a clustering application what are the desirable qualities (i.e., similarity indices) to have. This work argues that there are so many clustering similarity indices with (sometimes) disagreements among them. The authors run experiments on 16 ... | SP:916fbf4e8da5fb73f5012ec5711662cd9be2e067 |
Systematic Analysis of Cluster Similarity Indices: How to Validate Validation Measures | 1 INTRODUCTION . Clustering is an unsupervised machine learning problem , where the task is to group objects that are similar to each other . In network analysis , a related problem is called community detection , where grouping is based on relations between items ( links ) , and the obtained clusters are expected to b... | Cluster Similarity Indices (CSIs) take as input two clusterings A, B and assign a similarity score for the given pair of clusterings. The index calculates a score based on the number of pairs of elements that clustered together on both clustering (N++), those that are not clustered together in non of A,B (N--), those t... | SP:916fbf4e8da5fb73f5012ec5711662cd9be2e067 |
Generalization bounds via distillation | 1 OVERVIEW AND MAIN RESULTS . Generalization bounds are statistical tools which take as input various measurements of a predictor on training data , and output a performance estimate for unseen data — that is , they estimate how well the predictor generalizes to unseen data . Despite extensive development spanning many... | The paper overall is of good quality. The story of the work is well-written which makes the contributions easier to digest. One suggestion would be to comment a bit more on the relevance of the margin distribution for readers that are unfamiliar with it, for instance, in Figure 1, the term margin distribution is thrown... | SP:813473d94da9db192e13548da7f92149773062a5 |
Generalization bounds via distillation | 1 OVERVIEW AND MAIN RESULTS . Generalization bounds are statistical tools which take as input various measurements of a predictor on training data , and output a performance estimate for unseen data — that is , they estimate how well the predictor generalizes to unseen data . Despite extensive development spanning many... | The generalization performance of learning algorithms characterizes their ability to generalize their empirical behavior on training examples to unseen test data, which provides an intuitive understanding of how different parameters affect the learning performance and some guides to design learning machines. Different ... | SP:813473d94da9db192e13548da7f92149773062a5 |
Counterfactual Thinking for Long-tailed Information Extraction | 1 INTRODUCTION . The goal of Information Extraction ( IE ) ( Sarawagi , 2008 ; Chiticariu et al. , 2013 ) is to detect the structured information from unstructured texts . IE tasks , such as named entity recognition ( NER ) ( Lample et al. , 2016 ) , relation extraction ( RE ) ( Zeng et al. , 2014 ; Peng et al. , 2017 ... | This paper proposes a novel model integrating both causal inference and structure-aware counterfactual training to enhance the long-tail performances of information extraction. The causal mechanism considers a structured causal model that takes into account all possible cause-effect relations for the final predictions,... | SP:0fa59beb93e339dc3612719931b206653916b8b5 |
Counterfactual Thinking for Long-tailed Information Extraction | 1 INTRODUCTION . The goal of Information Extraction ( IE ) ( Sarawagi , 2008 ; Chiticariu et al. , 2013 ) is to detect the structured information from unstructured texts . IE tasks , such as named entity recognition ( NER ) ( Lample et al. , 2016 ) , relation extraction ( RE ) ( Zeng et al. , 2014 ; Peng et al. , 2017 ... | The novelty of the paper seems to be in application of the counterfactual analysis to address the long-tailed IE issues, which might be interesting to the IE researchers. Overall, more theory about the counterfactual generation for IE task should be added, for this is what the novelty of the paper; also, for the rebala... | SP:0fa59beb93e339dc3612719931b206653916b8b5 |
The geometry of integration in text classification RNNs | 1 INTRODUCTION . Modern recurrent neural networks ( RNNs ) can achieve strong performance in natural language processing ( NLP ) tasks such as sentiment analysis , document classification , language modeling , and machine translation . However , the inner workings of these networks remain largely mysterious . As RNNs a... | This paper sheds light on how trained RNNs solve text classification problems by analyzing them from a dynamical systems perspective. It extends recent work where a similar analysis was applied to the simpler setting of binary sentiment classification. When projecting the RNN hidden states to principal dimensions that ... | SP:b2f83cd755f4da835e943237e2ba6faf69e8008a |
The geometry of integration in text classification RNNs | 1 INTRODUCTION . Modern recurrent neural networks ( RNNs ) can achieve strong performance in natural language processing ( NLP ) tasks such as sentiment analysis , document classification , language modeling , and machine translation . However , the inner workings of these networks remain largely mysterious . As RNNs a... | This paper presents an analysis on the trained recurrent neural networks (RNN) especially for NLP classification problems. The analysis takes the dynamical systems point of view and investigates the dynamics by looking at the Jacobians around the fixed points. This work founds low dimensionalility and attractor dynamic... | SP:b2f83cd755f4da835e943237e2ba6faf69e8008a |
Deep Kernel Processes | 1 INTRODUCTION . The deep learning revolution has shown us that effective performance on difficult tasks such as image classification ( Krizhevsky et al. , 2012 ) requires deep models with flexible lower-layers that learn task-dependent representations . Here , we consider whether these insights from the neural network... | This paper proposes deep kernel processes (DKPs), which can be viewed as a specific kind of deep Gaussian processes where the kernel can be written as a function of the Gram matrix. The features in the intermediate layers are integrated out and the Gram matrix are Wishart distributed. A doubly stochastic variational in... | SP:40e4749c3e5c57e12a6c540510b74ae3551e479a |
Deep Kernel Processes | 1 INTRODUCTION . The deep learning revolution has shown us that effective performance on difficult tasks such as image classification ( Krizhevsky et al. , 2012 ) requires deep models with flexible lower-layers that learn task-dependent representations . Here , we consider whether these insights from the neural network... | This paper proposes a prior distribution over covariance matrices of kernels which is defined as a sequential graphical model where each variable is Wishart distributed and its scale matrix is a non-linear transformation of its predecesor variable on the graph. The paper begins by considering a DGP with isotropic kerne... | SP:40e4749c3e5c57e12a6c540510b74ae3551e479a |
Continuous Transfer Learning | 1 INTRODUCTION Source domain Target domain 𝓓𝓓𝑺𝑺 𝓓𝓓𝑻𝑻𝟏𝟏 𝓓𝓓𝑻𝑻𝟐𝟐 𝓓𝓓𝑻𝑻𝒕𝒕 𝓓𝓓𝑻𝑻𝒏𝒏⋯⋯𝓓𝓓𝑻𝑻𝟑𝟑 Negative transfer 𝓓𝓓𝑺𝑺 Figure 1 : Illustration of continuous transfer learning . It learns a predictive function in DTt using knowledge from both source domain DS and historical target domain DTi ( ... | The paper proposed a transfer learning setting where the target domain varies/evolves over time and the source domain is considered static. The paper uses C-divergence to measure label-dependent domain discrepancy between source/previous target domain and the current target domain and provided a theoretical bound. The ... | SP:cc84a9b9b02da8079787f6e5de7e1b83d95e8d5f |
Continuous Transfer Learning | 1 INTRODUCTION Source domain Target domain 𝓓𝓓𝑺𝑺 𝓓𝓓𝑻𝑻𝟏𝟏 𝓓𝓓𝑻𝑻𝟐𝟐 𝓓𝓓𝑻𝑻𝒕𝒕 𝓓𝓓𝑻𝑻𝒏𝒏⋯⋯𝓓𝓓𝑻𝑻𝟑𝟑 Negative transfer 𝓓𝓓𝑺𝑺 Figure 1 : Illustration of continuous transfer learning . It learns a predictive function in DTt using knowledge from both source domain DS and historical target domain DTi ( ... | This paper studies how to transfer the information in the static source domain to the time-evolving target domain. This paper proposes a domain discrepancy measure and an algorithm for continuous transfer learning. The results seem to be interesting and the problem this paper studies is important. However, the domain r... | SP:cc84a9b9b02da8079787f6e5de7e1b83d95e8d5f |
BAFFLE: TOWARDS RESOLVING FEDERATED LEARNING’S DILEMMA - THWARTING BACKDOOR AND INFERENCE ATTACKS | 1 INTRODUCTION . Federated learning ( FL ) is an emerging collaborative machine learning trend with many applications such as next word prediction for mobile keyboards ( McMahan & Ramage , 2017 ) , medical imaging ( Sheller et al. , 2018a ) , and intrusion detection for IoT ( Nguyen et al. , 2019 ) . In FL , clients lo... | In the paper, the authors proposed a novel privacy-preserving defense approach BAFFLE for federated learning which could simultaneously impede backdoor and inference attacks. To impede backdoor attacks, the Model Filtering layer (i.e., by dynamic clustering) and Poison Elimination layer (i.e., by noising and clipping) ... | SP:aeb3b57c2e2f7f7dfba24ee77e4aab2f445b947f |
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