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A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference | 1 INTRODUCTION . The inference-time computational demands of deep neural networks ( DNNs ) are increasing , owing to the “ going deeper '' ( Szegedy et al. , 2015 ) strategy for improving accuracy : as a DNN gets deeper , it progressively gains the ability to learn higher-level , complex representations . This strategy... | This paper studies adversarial attack and defense for adaptive multi-exit network. Adaptive multi-exit network is, by itself, a pretty new and under-studied topic, let alone the adversarial study on top of it. This paper proposes a simple-yet-effective DeepSloth attack based on layerwise loss function. It also proposed... | SP:cb32c18a6a766894aa23e1f84ea9c38ef21fe023 |
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction | 1 INTRODUCTION . Stochastic gradient Monte Carlo methods ( Welling & Teh , 2011 ; Chen et al. , 2014 ; Li et al. , 2016 ) are the golden standard for Bayesian inference in deep learning due to their theoretical guarantees in uncertainty quantification ( Vollmer et al. , 2016 ; Chen et al. , 2015 ) and non-convex optimi... | The paper presents VR-reSGLD, a method to accelerate replica exchange stochastic gradient Langevin diffusion (reSGLD), which has been proposed recently to tackle non-convex learning problems. reSGLD suffers from two major sources of error resulting in low swapping rates: minibatch noise and the discretization error of ... | SP:5817f96548dd3269127fe57d136b38735e6acea8 |
Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction | 1 INTRODUCTION . Stochastic gradient Monte Carlo methods ( Welling & Teh , 2011 ; Chen et al. , 2014 ; Li et al. , 2016 ) are the golden standard for Bayesian inference in deep learning due to their theoretical guarantees in uncertainty quantification ( Vollmer et al. , 2016 ; Chen et al. , 2015 ) and non-convex optimi... | The authors propose a variant of the Replica Exchange Stochastic Gradient Langevin Dynamics (reSGLD) for non log-concave sampling by using a variant reduction technique on the estimation of the swapping rate. Assuming that the log-density is a finite sum. the authors apply classical variance reduction techniques to the... | SP:5817f96548dd3269127fe57d136b38735e6acea8 |
Sobolev Training for the Neural Network Solutions of PDEs | 1 INTRODUCTION . Deep learning has achieved remarkable success in many scientific fields , including computer vision and natural language processing . In addition to engineering , deep learning has been successfully applied to the field of scientific computing . Particularly , the use of neural networks for the numeric... | The idea of using neural networks to approximate the solutions of the pdes is very interesting, specially in high-dimensional setting where classical approaches fail to scale. Although there has been many efforts in this direction, there are still open venues to explore. One of the most important aspect is the choice o... | SP:41f2c5e8a9a38a38a14436b57ba124e1b69ff3d2 |
Sobolev Training for the Neural Network Solutions of PDEs | 1 INTRODUCTION . Deep learning has achieved remarkable success in many scientific fields , including computer vision and natural language processing . In addition to engineering , deep learning has been successfully applied to the field of scientific computing . Particularly , the use of neural networks for the numeric... | Sobolev training of neural networks, which augments the standard loss function with terms that penalize discrepancies between the derivatives of the network and target functions, has been shown empirically to improve data-efficiency. Intuitively, one would expect that it also aids generalization in settings where the t... | SP:41f2c5e8a9a38a38a14436b57ba124e1b69ff3d2 |
Mixed-Features Vectors and Subspace Splitting | Motivated by metagenomics , recommender systems , dictionary learning , and related problems , this paper introduces subspace splitting ( SS ) : the task of clustering the entries of what we call a mixed-features vector , that is , a vector whose subsets of coordinates agree with a collection of subspaces . We derive p... | The paper introduces the problem of subspace spitting, in which an observed mixed-features vector is to be partitioned such that the identified partitions match with given subspaces. The main results of the paper lie in deriving sufficient and necessary conditions for identifiability of these partitions when the subspa... | SP:fe56b465b11b4f99ed9eb8bd07d08254e8603a80 |
Mixed-Features Vectors and Subspace Splitting | Motivated by metagenomics , recommender systems , dictionary learning , and related problems , this paper introduces subspace splitting ( SS ) : the task of clustering the entries of what we call a mixed-features vector , that is , a vector whose subsets of coordinates agree with a collection of subspaces . We derive p... | The authors propose a method to perform subspace splitting. That is, the task of clustering the entries of an input vector into sets of coherent subspaces. The contribution of the work is two-fold: (1) the theoretical characterization of the problem, and its well-posedness, and (2) the presentation of three algorithms ... | SP:fe56b465b11b4f99ed9eb8bd07d08254e8603a80 |
Learning Chess Blindfolded | 1 INTRODUCTION . Recently , transformer-based language models such as GPT-3 have stretched notions of what is possible with the simple self-supervised objective of language modeling , becoming a fixture in state of the art language technologies ( Vaswani et al. , 2017 ; Devlin et al. , 2019 ; Brown et al. , 2020 ) . Ho... | This paper explores learning chess from raw notation as a benchmark for the ability of language models to track world state. Chess is an interesting benchmark, as a set of moves can be unambiguously linked to a world state, there are large amounts of data available and the model can easily be probed for its board track... | SP:001a31f7a439ab22943dedb4fa4d46e3dd56e603 |
Learning Chess Blindfolded | 1 INTRODUCTION . Recently , transformer-based language models such as GPT-3 have stretched notions of what is possible with the simple self-supervised objective of language modeling , becoming a fixture in state of the art language technologies ( Vaswani et al. , 2017 ; Devlin et al. , 2019 ; Brown et al. , 2020 ) . Ho... | This paper is an interesting exploratory study analyzing the ability of language models to track the state of a chessboard. The authors adopt a clever chess notation which allows them to probe the language model's state tracking ability by looking at its next word prediction (akin to probes in [1]). Quite remarkably, l... | SP:001a31f7a439ab22943dedb4fa4d46e3dd56e603 |
L2E: Learning to Exploit Your Opponent | 1 INTRODUCTION . One core research topic in modern artificial intelligence is creating agents that can interact effectively with their opponents in different scenarios . To achieve this goal , the agents should have the ability to reason about their opponents ’ behaviors , goals , and beliefs . Opponent modeling , whic... | This paper proposes the Learning to Exploit (L2E) framework that can quickly adapt to diverse opponent's unknown strategies. The main contributions of L2E include: 1. learning of the base model based on the optimization similar to MAML (Finn et al., ICML-17) to adapt to a new opponent after a few learning iterations (S... | SP:1ddcef5a08ed53d730119a0b591e2cb092c422eb |
L2E: Learning to Exploit Your Opponent | 1 INTRODUCTION . One core research topic in modern artificial intelligence is creating agents that can interact effectively with their opponents in different scenarios . To achieve this goal , the agents should have the ability to reason about their opponents ’ behaviors , goals , and beliefs . Opponent modeling , whic... | The authors propose an opponent modeling in 1-vs-1 games called the Learning to Exploit (L2E) framework, which exploits opponents by a few interactions with different opponents during training so that it can adapt to new opponents with unknown styles during testing quickly. In particular, the authors propose Opponent S... | SP:1ddcef5a08ed53d730119a0b591e2cb092c422eb |
Multilayer Dense Connections for Hierarchical Concept Classification | 1 INTRODUCTION . Classification is a core concept for numerous computer vision tasks . Given the convolutional features , different architectures classify either the image itself ( He et al. , 2015 ; Szegedy et al. , 2016 ) , the region/bounding boxes for object detection ( He et al. , 2017 ; Liu et al. , 2015 ) , or ,... | The paper proposes a method to learn concept classes along with its concept superclasses. The proposed method relies on an ontology which they heuristically re-organize by essentially pruning nodes that have few descendants and large semantic overlap. The network proposed to model the ontology essentially just consists... | SP:2cd4e9bce03b98ab2d321dca8628eeacfada140a |
Multilayer Dense Connections for Hierarchical Concept Classification | 1 INTRODUCTION . Classification is a core concept for numerous computer vision tasks . Given the convolutional features , different architectures classify either the image itself ( He et al. , 2015 ; Szegedy et al. , 2016 ) , the region/bounding boxes for object detection ( He et al. , 2017 ; Liu et al. , 2015 ) , or ,... | This paper designs a multilayer connection structure for neural networks, such that the connection architecture supports implementation of a hierarchical classification scheme within these layers. It applies this design to the task of hierarchical classification on ImageNet. Experiments compare results with those of ... | SP:2cd4e9bce03b98ab2d321dca8628eeacfada140a |
Deep Q Learning from Dynamic Demonstration with Behavioral Cloning | 1 INTRODUCTION . Deep reinforcement learning ( DRL ) methods have made great progress ( Mnih et al. , 2013 ; 2015 ; Silver et al. , 2017 ) when applied in several rule-based applications such as the Go game ( Silver et al. , 2016 ) . However , due to the diversity and uncertainty of complex systems , the establishment ... | This paper proposes integrating deep Q-learning from dynamic demonstrations with a behavioral cloning model (DQfDD-BC). Compared with DQfD, the proposed approach introduces a behavior cloning model, which was first pre-trained by leveraging historical demonstrations and then updated using generated dynamic demonstratio... | SP:183f775e3c461066fc88446883bb458fb8d6d607 |
Deep Q Learning from Dynamic Demonstration with Behavioral Cloning | 1 INTRODUCTION . Deep reinforcement learning ( DRL ) methods have made great progress ( Mnih et al. , 2013 ; 2015 ; Silver et al. , 2017 ) when applied in several rule-based applications such as the Go game ( Silver et al. , 2016 ) . However , due to the diversity and uncertainty of complex systems , the establishment ... | This paper is introducing a learning method which combines both Imitation Learning and Reinforcement Learning, such that an autonomous learner can leverage prerecorded expert knowledge. In comparison to previous work, this model has an expert cost function which gives priority to the expert behavior, not only using the... | SP:183f775e3c461066fc88446883bb458fb8d6d607 |
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization | 1 INTRODUCTION . By reducing the number of bits needed to represent a model parameter of Deep Neural Networks ( DNN ) , quantization ( Lin et al. , 2016 ; Park et al. , 2017 ; Han et al. , 2015 ; Zhou et al. , 2018 ; Zhu et al. , 2016 ; Hwang & Sung , 2014 ; Wu et al. , 2016 ; Zhang et al. , 2018 ; Köster et al. , 2017... | This paper describes an improved way to determine weight quantization bit lengths using reinforcement learning, by injecting model evaluation directly into action selection. Building upon a DRL setup where the action at each timestep corresponds to selecting a bit value for each layer, the method adds a "Q-value indic... | SP:a84141dacf9260f8c5dede0959fd4f58f29a51dd |
Simple Augmentation Goes a Long Way: ADRL for DNN Quantization | 1 INTRODUCTION . By reducing the number of bits needed to represent a model parameter of Deep Neural Networks ( DNN ) , quantization ( Lin et al. , 2016 ; Park et al. , 2017 ; Han et al. , 2015 ; Zhou et al. , 2018 ; Zhu et al. , 2016 ; Hwang & Sung , 2014 ; Wu et al. , 2016 ; Zhang et al. , 2018 ; Köster et al. , 2017... | This paper studies the DNN quantization using deep reinforcement learning. The paper proposes an augmented DRL which introduces a Q-value indicator to refine action selection. The proposed approach has been applied to several image classification baselines and has compared with several recent DRL based quantization app... | SP:a84141dacf9260f8c5dede0959fd4f58f29a51dd |
Convergent Adaptive Gradient Methods in Decentralized Optimization | 1 INTRODUCTION . Distributed training of machine learning models is drawing growing attention in the past few years due to its practical benefits and necessities . Given the evolution of computing capabilities of CPUs and GPUs , computation time in distributed settings is gradually dominated by the communication time i... | The paper introduces a decentralized framework for adaptive momentum-based gradient descent optimizers, such as ADAM. The proposed method is novel and is among the first works to consider a decentralized communication graph without a master node. The author discovers the divergent properties of the recent work of DADAM... | SP:63bd51b9796b118e53bf1bff71c405f61f210e9f |
Convergent Adaptive Gradient Methods in Decentralized Optimization | 1 INTRODUCTION . Distributed training of machine learning models is drawing growing attention in the past few years due to its practical benefits and necessities . Given the evolution of computing capabilities of CPUs and GPUs , computation time in distributed settings is gradually dominated by the communication time i... | In this paper, the authors attempt to use adaptive gradient methods in decentralized training paradigm. They develop a general framework to convert an adaptive gradient method from a centralized one to its decentralized variant. Specifically, they propose a decentralized AMSGrad algorithm. They also point out a potenti... | SP:63bd51b9796b118e53bf1bff71c405f61f210e9f |
Contextual Image Parsing via Panoptic Segment Sorting | Visual context is versatile and hard to describe or label precisely . We aim to leverage the densely labeled task , image parsing , a.k.a panoptic segmentation , to learn a model that encodes and discovers object-centric context . Most existing approaches based on deep learning tackle image parsing via fusion of pixel-... | The paper presents a pixel-wise embedding strategy for panoptic segmentation, which aims to learn a pixel representation that encodes both semantic and instance information. To this end, the proposed method builds on top of the Segment Sorting approach and extends its contrastive loss to the instance level by utilizing... | SP:37afbad2d7a35ae747b29b2bef8e0b87dc82bfa6 |
Contextual Image Parsing via Panoptic Segment Sorting | Visual context is versatile and hard to describe or label precisely . We aim to leverage the densely labeled task , image parsing , a.k.a panoptic segmentation , to learn a model that encodes and discovers object-centric context . Most existing approaches based on deep learning tackle image parsing via fusion of pixel-... | This paper adapts Segment Sorting to panoptic segmentation and proposes a Panoptic Segment Sorting (PSS). The proposed method learns to sort segments according to both of its semantic and instance labels. The semantic label is acquired by simply mapping and classifying prototype feature and instances are formed by a cl... | SP:37afbad2d7a35ae747b29b2bef8e0b87dc82bfa6 |
Explainable Reinforcement Learning Through Goal-Based Interpretability | 1 INTRODUCTION . Deep learning has had a huge impact on Reinforcement Learning , making it possible to solve certain problems for the first time , vastly improving performance in many old problems and often exceeding human performance in difficult tasks ( Schrittwieser et al. , 2019 ; Badia et al. , 2020 ) . These impr... | The paper proposes a hybrid imitation learning/reinforcement learning method for learning hierarchical policies where the top layer provides sub-goals and desired cumulative rewards and the bottom layer learns to meet these goals. The advantage of such a decomposition is interpretability of the learned policy. The algo... | SP:b669fac24df0cab2ca31e638ea1b336e5af40866 |
Explainable Reinforcement Learning Through Goal-Based Interpretability | 1 INTRODUCTION . Deep learning has had a huge impact on Reinforcement Learning , making it possible to solve certain problems for the first time , vastly improving performance in many old problems and often exceeding human performance in difficult tasks ( Schrittwieser et al. , 2019 ; Badia et al. , 2020 ) . These impr... | This paper proposes a hierarchical RL method where the high level controller produces a series of sub-goals in an open-loop fashion which the low-level controller attempts to reach sequentially with the aim of maximising task rewards. The agent is trained using an extension of Hindsight Actor-Critic (HAC) algorithm. Th... | SP:b669fac24df0cab2ca31e638ea1b336e5af40866 |
Learning Deeply Shared Filter Bases for Efficient ConvNets | 1 INTRODUCTION . Modern networks such as ResNets usually have massive identical convolution blocks and recent analytic studies ( Jastrzebski et al. , 2018 ) show that these blocks perform similar iterative refinement rather than learning new features . Inspired by these massive identical block structure of modern netwo... | This method proposes to decompose convolutional filters using a low rank filter basis where the convolutional operation in a layer consists of shareable filter basis and non-shareable layer coefficients. This is developped to save computational costs whilst maintaining performance. To regularise against vanishing/explo... | SP:50759dd814d98ba988b7cc423e3115d62e05db47 |
Learning Deeply Shared Filter Bases for Efficient ConvNets | 1 INTRODUCTION . Modern networks such as ResNets usually have massive identical convolution blocks and recent analytic studies ( Jastrzebski et al. , 2018 ) show that these blocks perform similar iterative refinement rather than learning new features . Inspired by these massive identical block structure of modern netwo... | This paper addresses the problem of obtaining more compact CNNs by a parameter sharing method. The authors propose to represent a weight filter in a low-rank subspace (represented as a linear combination of low-rank filter basis) plus a set of non-shared low-rank filter basis (per-layer). In this way, the shared low-ra... | SP:50759dd814d98ba988b7cc423e3115d62e05db47 |
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients | 1 INTRODUCTION . Many MCMC methods use physics-inspired evolution such as Langevin dynamics ( Brooks et al. , 2011 ) to utilize gradient information for exploring posterior distributions over continuous parameter space efficiently . However , gradient-based MCMC methods are often limited by the computational cost of co... | The paper proposes an alternative to the uniform sampling scheme used for constructing mini-batches in stochastic gradient sampling algorithms. The proposed scheme, called Exponentially Weighted Stochastic Gradient (EWSG), is devised such its transition kernel matches that of the batch gradient descent. The proposed sc... | SP:b9919f153a64663a2bfaf12303c660d995694591 |
Improving Sampling Accuracy of Stochastic Gradient MCMC Methods via Non-uniform Subsampling of Gradients | 1 INTRODUCTION . Many MCMC methods use physics-inspired evolution such as Langevin dynamics ( Brooks et al. , 2011 ) to utilize gradient information for exploring posterior distributions over continuous parameter space efficiently . However , gradient-based MCMC methods are often limited by the computational cost of co... | This paper proposes a non-uniform sampling method for stochastic gradient minibatches for SG-MCMC. By sampling the indices of the stochastic gradients according to a parameter-specific (exponentially weighted) non-uniform distribution, the paper shows that it exactly matches the transition kernel of full batch gradient... | SP:b9919f153a64663a2bfaf12303c660d995694591 |
NNGeometry: Easy and Fast Fisher Information Matrices and Neural Tangent Kernels in PyTorch | Practical and theoretical advances in deep learning have been accelerated by the development of an ecosystem of libraries allowing practitioners to focus on developing new techniques instead of spending weeks or months re-implementing the wheel . In particular , automatic differentiation frameworks such as Theano ( Ber... | This paper introduces a new PyTorch library for computing Fisher Information Matrices and Neural Tangent Kernel (NTK) in deep learning; with applications ranging from Frobenius norm regularization, second-order optimization, and generalization analysis. The authors begin by providing a background on Fisher matrices and... | SP:f2bede94ecbf910aeafa0355e1d2576f5716dc7d |
NNGeometry: Easy and Fast Fisher Information Matrices and Neural Tangent Kernels in PyTorch | Practical and theoretical advances in deep learning have been accelerated by the development of an ecosystem of libraries allowing practitioners to focus on developing new techniques instead of spending weeks or months re-implementing the wheel . In particular , automatic differentiation frameworks such as Theano ( Ber... | This paper describes a new PyTorch package, NNGeometry, for computing complicated neural network objects, such as the Fisher Information Matrix (FIM) and the Neural Tangent Kernel (NTK). The package uses an abstract representation to allow the user to implicitly choose between different approximations to these objects ... | SP:f2bede94ecbf910aeafa0355e1d2576f5716dc7d |
Distributional Sliced-Wasserstein and Applications to Generative Modeling | Sliced-Wasserstein distance ( SW ) and its variant , Max Sliced-Wasserstein distance ( Max-SW ) , have been used widely in the recent years due to their fast computation and scalability even when the probability measures lie in a very high dimensional space . However , SW requires many unnecessary projection samples to... | The paper presents a novel variant of the Sliced Wasserstein (SW) distance. Wasserstein distances have been used recently in lot of machine learning problems. One of the major problem is that, in its primal form, it is computationally expensive. In order to alleviate this problem, a class of methods, called sliced, lev... | SP:6fe5ce1a3c0f3a9a80bad30444dc2d51482b3b11 |
Distributional Sliced-Wasserstein and Applications to Generative Modeling | Sliced-Wasserstein distance ( SW ) and its variant , Max Sliced-Wasserstein distance ( Max-SW ) , have been used widely in the recent years due to their fast computation and scalability even when the probability measures lie in a very high dimensional space . However , SW requires many unnecessary projection samples to... | The paper describes a family of sliced Wasserstein divergences that maximize the distribution over slices subject to constraints on the concentration of slices. Extremes of the family are the sliced Wasserstein and max-sliced Wasserstein distance. In between these the divergence is sensitive to informative discrepancie... | SP:6fe5ce1a3c0f3a9a80bad30444dc2d51482b3b11 |
Task-similarity Aware Meta-learning through Nonparametric Kernel Regression | 1 INTRODUCTION . Meta-learning seeks to abstract a general learning rule applicable to a class of different learning problems or tasks , given the knowledge of a set of training tasks from the class ( Finn & Levine , 2018 ; Denevi et al. , 2018 ; Hospedales et al. , 2020 ; Grant et al. , 2018 ; Yoon et al. , 2018 ) . T... | The paper introduced a meta-learning framework in which a kernel describing similarity between the tasks is used to construct an RKHS which is used to perform kernel regression. The framework is instantiated in a form of an algorithm: TANML which can be viewed as an extension to a popular Meta-SGD algorithm. The experi... | SP:df4c28b42c8505f42804ad298a1b51ebb060ea32 |
Task-similarity Aware Meta-learning through Nonparametric Kernel Regression | 1 INTRODUCTION . Meta-learning seeks to abstract a general learning rule applicable to a class of different learning problems or tasks , given the knowledge of a set of training tasks from the class ( Finn & Levine , 2018 ; Denevi et al. , 2018 ; Hospedales et al. , 2020 ; Grant et al. , 2018 ; Yoon et al. , 2018 ) . T... | This paper proposes a theoretical formulation for meta-learning that uses task similarity based on task gradients, which helps learning in the presence of outlier tasks. The inner loop parameter update is given by linear kernel regression, where the kernel function computes similarity between gradients of different tas... | SP:df4c28b42c8505f42804ad298a1b51ebb060ea32 |
Learning Robust Models by Countering Spurious Correlations | 1 INTRODUCTION . Machine learning , especially deep neural networks , has demonstrated remarkable empirical successes over various benchmarks . One promising next step is to extend such empirical achievements beyond i.i.d benchmarks . If we train a model with data from one distribution ( i.e. , the source distribution ... | This paper studies the problem that spurious features in the training set can cause accuracy drop in the test phase. They formalize a generalization error bound for this setup and provide two solutions, one principled solution with the knowledge of spurious correlated features and one minimal supervision (MS) method wi... | SP:2ec2433a907a60ebfbf9ffefc72b70eb76c1f591 |
Learning Robust Models by Countering Spurious Correlations | 1 INTRODUCTION . Machine learning , especially deep neural networks , has demonstrated remarkable empirical successes over various benchmarks . One promising next step is to extend such empirical achievements beyond i.i.d benchmarks . If we train a model with data from one distribution ( i.e. , the source distribution ... | This paper formalizes a new generalization error bound for the problem of spurious correlation (a.k.a. confounding factors or dataset bias) and shows that it is tighter than the well-established domain adaptation one under realistic assumptions. The analysis leads to a set of solutions linking to established solutions.... | SP:2ec2433a907a60ebfbf9ffefc72b70eb76c1f591 |
Fast Partial Fourier Transform | In this paper , we propose a fast Partial Fourier Transform ( PFT ) , an efficient algorithm for computing only a part of Fourier coefficients . PFT approximates a part of twiddle factors ( trigonometric constants ) using polynomials , thereby reducing the computational complexity due to the mixture of many twiddle fac... | The paper presents a fast approximate algorithm for partial discrete Fourier transform. Given the input signal $a_0...a_N$ in the time domain the algorithm approximately computes the first $O(M)$ frequencies. The running time is $O(r (N + M \log M))$, where $r$ a parameter that controls the accuracy of the output. The ... | SP:fc497267ee411f936495a4b404ed87752f12687f |
Fast Partial Fourier Transform | In this paper , we propose a fast Partial Fourier Transform ( PFT ) , an efficient algorithm for computing only a part of Fourier coefficients . PFT approximates a part of twiddle factors ( trigonometric constants ) using polynomials , thereby reducing the computational complexity due to the mixture of many twiddle fac... | The paper suggests a method for quickly computing a "partial Fourier transform", which basically means that we want only a small range of output frequencies. The main technique is an approximation of so called "twiddle functions" (which are basically trigonometric functions, or, exponents of complex units if viewed in... | SP:fc497267ee411f936495a4b404ed87752f12687f |
SVMax: A Feature Embedding Regularizer | 1 INTRODUCTION . A neural network ’ s knowledge is embodied in both its weights and activations . This difference manifests in how network pruning and knowledge distillation tackle the model compression problem . While pruning literature Li et al . ( 2016 ) ; Luo et al . ( 2017 ) ; Yu et al . ( 2018 ) compresses models... | This paper proposes a new approach to regularize the feature embedding of neural networks. The proposed regularizer, maximizes the mean singular value of the feature matrix per batch, leading to a uniform spread of features. This enables learning with larger learning rates without the risk of model collapse. Authors de... | SP:3498af23a51d9522d5727025750c462e114f5566 |
SVMax: A Feature Embedding Regularizer | 1 INTRODUCTION . A neural network ’ s knowledge is embodied in both its weights and activations . This difference manifests in how network pruning and knowledge distillation tackle the model compression problem . While pruning literature Li et al . ( 2016 ) ; Luo et al . ( 2017 ) ; Yu et al . ( 2018 ) compresses models... | This paper proposes a regularization technique called SVMax (singular value maximization) that can mitigate model collapse and enable large learning rates to reduce training computation costs. The singular value decomposition of network activation is used to regularize the embedding space with unit circle embedding ass... | SP:3498af23a51d9522d5727025750c462e114f5566 |
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training | 1 INTRODUCTION . Locality Sensitive Hashing ( LSH ) has been adapted to address the computational and memory bottlenecks of large-scale neural network ( NN ) training in natural language processing ( Chandar et al. , 2016 ; Rae et al. , 2016 ; Kitaev et al. , 2020 ) , computer vision ( Chen et al. , 2015 ) and recommen... | The authors make a good insight into the slowly changing of Locality-Sensitive Hashing (LSH) hash codes for the weights (or model parameters) during the Neural Network (NN) training. With this new insight, they introduce a framework Mongoose with a newly designed schedule mechanism to reduce the LSH update overhead. Th... | SP:de05c7b7b8830e38da4254af1e0ca2ddadb50134 |
MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training | 1 INTRODUCTION . Locality Sensitive Hashing ( LSH ) has been adapted to address the computational and memory bottlenecks of large-scale neural network ( NN ) training in natural language processing ( Chandar et al. , 2016 ; Rae et al. , 2016 ; Kitaev et al. , 2020 ) , computer vision ( Chen et al. , 2015 ) and recommen... | Some neural network runs involve layers with a large number of neurons. These require large matrix-vector or matrix-multiplication which can slow their training/inference. However, if the output of mat-vec/mul is dominated by a few neurons with which the activation has large inner product (a matmul can be thought of as... | SP:de05c7b7b8830e38da4254af1e0ca2ddadb50134 |
Similarity Search for Efficient Active Learning and Search of Rare Concepts | 1 INTRODUCTION . Large-scale unlabeled datasets contain millions or billions of examples spread over a wide variety of underlying concepts ( Chelba et al. , 2013 ; Zhu et al. , 2015 ; Zhang et al. , 2015 ; Wan et al. , 2019 ; Russakovsky et al. , 2015 ; Kuznetsova et al. , 2020 ; Thomee et al. , 2016 ; Abu-El-Haija et ... | This paper proposes a new method (SEALS) to accelerate the active learning and active search with the skewness of the cardinality of rare class compared to the large-scale datasets. To leverage this skewness, the authors restrict the candidate pool for labelling mainly from the nearest neighbours of the currently label... | SP:0a155411707f21e84ec5d4ec1d53e29d5a622074 |
Similarity Search for Efficient Active Learning and Search of Rare Concepts | 1 INTRODUCTION . Large-scale unlabeled datasets contain millions or billions of examples spread over a wide variety of underlying concepts ( Chelba et al. , 2013 ; Zhu et al. , 2015 ; Zhang et al. , 2015 ; Wan et al. , 2019 ; Russakovsky et al. , 2015 ; Kuznetsova et al. , 2020 ; Thomee et al. , 2016 ; Abu-El-Haija et ... | This paper proposes an active learning and active search approach that targets samples for rare classes in very large unlabeled datasets with highly imbalanced class distributions. This is a common scenario in real-world applications, where these rare situations can be critical to accurately categorize - ie endangered ... | SP:0a155411707f21e84ec5d4ec1d53e29d5a622074 |
Robust Loss Functions for Complementary Labels Learning | In ordinary-label learning , the correct label is given to each training sample . 1 Similarly , a complementary label is also provided for each training sample in 2 complementary-label learning . A complementary label indicates a class that the 3 example does not belong to . Robust learning of classifiers has been inve... | This paper deals with the problem of complementary label learning, that is, when we know the set of labels which a given observation does not belong to. In particular, the paper proposes a robust loss function and an algorithm for learning from complimentary labels. Results shown on MNIST and CIFAR datasets indicate th... | SP:b3803f35c83786a139be4422007de99c6e786cf3 |
Robust Loss Functions for Complementary Labels Learning | In ordinary-label learning , the correct label is given to each training sample . 1 Similarly , a complementary label is also provided for each training sample in 2 complementary-label learning . A complementary label indicates a class that the 3 example does not belong to . Robust learning of classifiers has been inve... | This paper studied a new problem, that is, learning from complementary labels. The goal is to predict a correct label for a given sample when only given complementary labels. On the basis of the ordinary-label learning, the authors defined "robust loss functions" for complementary-label learning: a a loss function is... | SP:b3803f35c83786a139be4422007de99c6e786cf3 |
LEAF: A Learnable Frontend for Audio Classification | 1 INTRODUCTION . Learning representations by backpropagation in deep neural networks has become the standard in audio understanding , ranging from automatic speech recognition ( ASR ) ( Hinton et al. , 2012 ; Senior et al. , 2015 ) to music information retrieval ( Arcas et al. , 2017 ) , as well as animal vocalizations... | This paper presents a new learnable representation fo audio signal classification and compares it to the classical mel-filterbanks representation and two other learnable representations on a broad range of audio classification tasks, from birdsongs to pitch, instrument, language or emotion recognition. The proposed rep... | SP:5a1ad3ed5e9e4e7c7b0f10530688f2f52ee76948 |
LEAF: A Learnable Frontend for Audio Classification | 1 INTRODUCTION . Learning representations by backpropagation in deep neural networks has become the standard in audio understanding , ranging from automatic speech recognition ( ASR ) ( Hinton et al. , 2012 ; Senior et al. , 2015 ) to music information retrieval ( Arcas et al. , 2017 ) , as well as animal vocalizations... | The paper shows a detailed interpretation on the relationship between each component of hand-crafted audio front-ends (such as mel-spectrograms) and learnable counterparts. To do that, they followed the narratives presented from the previous works such as SincNet and improved the model by changing the several component... | SP:5a1ad3ed5e9e4e7c7b0f10530688f2f52ee76948 |
Adaptive N-step Bootstrapping with Off-policy Data | 1 INTRODUCTION . The goal of reinforcement learning ( RL ) is to find an optimal policy by interacting with the environment . In order to do that , a RL algorithm needs to define a target , e.g. , Q function or value function , and update it iteratively to bootstrap from scratch . The challenge of designing an efficien... | The paper deals with an intriguing point in RL -- how to correctly choose an adaptive $n$ for $n$-step bootstrap. From such a paper one might expect theoretical results on the tradeoff between bias and variance in the presence of off-policyness. However, as opposed to the picture portrayed in the first section, no such... | SP:a7a5a8dc7d3cbf7628878c285640dc8d670f71cf |
Adaptive N-step Bootstrapping with Off-policy Data | 1 INTRODUCTION . The goal of reinforcement learning ( RL ) is to find an optimal policy by interacting with the environment . In order to do that , a RL algorithm needs to define a target , e.g. , Q function or value function , and update it iteratively to bootstrap from scratch . The challenge of designing an efficien... | This paper provides a novel algorithm to estimate the optimal value of $n$ for $n$-step temporal difference methods. The paper derives an optimal value of $n$ based on minimizing a bias term, then utilizes intuition to derive an online approximation algorithm. The paper compares its adaptive $n$-step algorithm against ... | SP:a7a5a8dc7d3cbf7628878c285640dc8d670f71cf |
Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning | 1 INTRODUCTION . Unsupervised representation learning techniques have led to unprecedented results in domains like computer vision ( Hénaff et al. , 2019 ; He et al. , 2019 ) and natural language processing ( Devlin et al. , 2019 ; Radford et al. , 2019 ) . These methods are commonly composed of two stages – an initial... | This paper proposed a transfer approach for reinforcement learning. The proposed approach leverages a policy pre-trained via Never Give Up (NGU) approach, and can facilitate learning challenging RL tasks including the ones with sparse reward. This paper presents many strong pieces of evidence that this approach can be ... | SP:407fd895712f394b12b56e886901748270d11bd3 |
Coverage as a Principle for Discovering Transferable Behavior in Reinforcement Learning | 1 INTRODUCTION . Unsupervised representation learning techniques have led to unprecedented results in domains like computer vision ( Hénaff et al. , 2019 ; He et al. , 2019 ) and natural language processing ( Devlin et al. , 2019 ; Radford et al. , 2019 ) . These methods are commonly composed of two stages – an initial... | In this work the author focus on transfer in RL via proposing to transfer knowledge through behavior instead of representations. They propose using coverage as an objective for the pre-training procedure. They then employ the NGU (Badia 2020) They also propose a method based on coverage pre-training for transfer and pr... | SP:407fd895712f394b12b56e886901748270d11bd3 |
New Bounds For Distributed Mean Estimation and Variance Reduction | ∑n v=1 xv , while min- imizing total communication cost . DME is a fundamental construct in distributed machine learning , and there has been considerable work on variants of this problem , especially in the context of distributed variance reduction for stochastic gradients in parallel SGD . Previous work typically ass... | The paper considers a particular setting of distributed mean estimation problem, where each party has a vector of potentially large $l_2$ norm, yet this vectors are fairly close to each other. The goal is to communicate as few bits as possible and estimate the mean of the vectors. Previous approaches had the dependence... | SP:e43bf2bece7193a5b7dda847f81f3e730dffae9b |
New Bounds For Distributed Mean Estimation and Variance Reduction | ∑n v=1 xv , while min- imizing total communication cost . DME is a fundamental construct in distributed machine learning , and there has been considerable work on variants of this problem , especially in the context of distributed variance reduction for stochastic gradients in parallel SGD . Previous work typically ass... | This paper studies the problem of mean estimation of n vectors in R^d in a distributed setting. There are n machines. Each has one data point, a vector x_v. The goal is to compute the mean of x_v’s for v = 1, …, n with as few bits of communication as possible. | SP:e43bf2bece7193a5b7dda847f81f3e730dffae9b |
FedMes: Speeding Up Federated Learning with Multiple Edge Servers | 1 INTRODUCTION . With the explosive growth in the numbers of smart phones , wearable devices and Internet of Things ( IoT ) sensors , a large portion of data generated nowadays is collected outside the cloud , especially at the distributed end-devices at the edge . Federated learning ( McMahan et al. , 2017 ; Konecny e... | In previous federated learning literature, people usually assume there is only one cloud server communicating with all edge nodes/clients. However, since each server has its own coverage in practice, the latency between the server and clients out of the coverage can be pretty long. This paper focuses on reducing the co... | SP:066ab85834cd54a685a860021521eb1ffe35e60f |
FedMes: Speeding Up Federated Learning with Multiple Edge Servers | 1 INTRODUCTION . With the explosive growth in the numbers of smart phones , wearable devices and Internet of Things ( IoT ) sensors , a large portion of data generated nowadays is collected outside the cloud , especially at the distributed end-devices at the edge . Federated learning ( McMahan et al. , 2017 ; Konecny e... | This paper considers federated learning for edge devices with multiple wireless edge servers. The paper proposes FedMes to leverage devices in overlapping areas covered by multiple edge servers. In particular, in FedMes, if a device is in the coverage area of multiple edge servers, the device receives current models fr... | SP:066ab85834cd54a685a860021521eb1ffe35e60f |
Fuzzy c-Means Clustering for Persistence Diagrams | 1 INTRODUCTION . Persistence diagrams , a concise representation of the topology of a point cloud with strong theoretical guarantees , have emerged as a new tool in the field of data analysis ( Edelsbrunner & Harer , 2010 ) . Persistence diagrams have been successfully used to analyse problems ranging from financial cr... | The authors propose a novel algorithm for the fuzzy clustering of persistence diagrams. To determine cluster centroids, the Wasserstein-2 distance is used to minimize the weighted Fr\’echet mean between a potential cluster center and all PDs considered for clustering. The authors proof convergence of the clustering alg... | SP:eab7f68e9d6170869645eb7ca01ee340cf97d7a0 |
Fuzzy c-Means Clustering for Persistence Diagrams | 1 INTRODUCTION . Persistence diagrams , a concise representation of the topology of a point cloud with strong theoretical guarantees , have emerged as a new tool in the field of data analysis ( Edelsbrunner & Harer , 2010 ) . Persistence diagrams have been successfully used to analyse problems ranging from financial cr... | The paper proposes a new clustering algorithm for persistence diagrams. They use fuzzy c means clustering. The motivation for fuzzy clustering is that it allows each datum (persistence diagram) to have weighted (soft) membership in different clusters. The partial membership value is the ratio of the distance to that cl... | SP:eab7f68e9d6170869645eb7ca01ee340cf97d7a0 |
Deep Coherent Exploration For Continuous Control | 1 INTRODUCTION . The balance of exploration and exploitation ( Kearns & Singh , 2002 ; Jaksch et al. , 2010 ) is a longstanding challenge in reinforcement learning ( RL ) . With insufficient exploration , states and actions with high rewards can be missed , resulting in policies prematurely converging to bad local opti... | This paper proposes Deep Coherent Exploration that unifies step-based exploration and trajectory-based exploration on continuous control. There exists a prior work that bridges a gap between the two exploration methods for linear policies, and this paper generalizes the prior work for various deep RL methods: on-policy... | SP:f410dbc73c4d044a0f3b113ca08f0a6e9e40b07a |
Deep Coherent Exploration For Continuous Control | 1 INTRODUCTION . The balance of exploration and exploitation ( Kearns & Singh , 2002 ; Jaksch et al. , 2010 ) is a longstanding challenge in reinforcement learning ( RL ) . With insufficient exploration , states and actions with high rewards can be missed , resulting in policies prematurely converging to bad local opti... | This paper focuses on undirected exploration strategies in reinforcement learning. Following the prior work, this paper proposes an exploration method unifying the step-based and trajectory-based exploration. The authors propose to perturb only the last(linear) layer of the policy for exploration, instead of perturbing... | SP:f410dbc73c4d044a0f3b113ca08f0a6e9e40b07a |
On the Universal Approximability and Complexity Bounds of Deep Learning in Hybrid Quantum-Classical Computing | With the continuously increasing number of quantum bits in quantum computers , there are growing interests in exploring applications that can harvest the power of them . Recently , several attempts were made to implement neural networks , known to be computationally intensive , in hybrid quantum-classical scheme comput... | The problem studied in this work is of interest in the quantum machine learning community, as the power of small and noisy quantum computers for machine learning problems is far from being understood. Therefore, it is important to study the expressivity of quantum neural networks as function approximators. This work us... | SP:19ad4889e4e13927f7a6e5ab01d0b0e1ef925337 |
On the Universal Approximability and Complexity Bounds of Deep Learning in Hybrid Quantum-Classical Computing | With the continuously increasing number of quantum bits in quantum computers , there are growing interests in exploring applications that can harvest the power of them . Recently , several attempts were made to implement neural networks , known to be computationally intensive , in hybrid quantum-classical scheme comput... | The paper considers the expressivity and approximation properties of machine learning models where a parameterized quantum circuit is used to 'accelerate' a classical neural network. The results consider a model with a date encoder, a quantum circuit, and then a classical feedforward neural net for post-processing. To ... | SP:19ad4889e4e13927f7a6e5ab01d0b0e1ef925337 |
What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator | 1 INTRODUCTION . Reinforcement learning ( RL ) has been widely considered as a promising way to learn optimal policies in many decision making problems ( Mnih et al. , 2015 ; Lillicrap et al. , 2015 ; Silver et al. , 2016 ; You et al. , 2018 ; Schreck et al. , 2019 ; Vinyals et al. , 2019 ; Hafner et al. , 2020 ) . Lyi... | The authors propose PeVFA: a value function able to evaluate the expected return of multiple policies. They do so by extending the conventional value function, allowing it to receive as input the parameter (or a representation) of the policy. The authors study the local generalization property of PeVFA, propose possibl... | SP:763cf5ceb0330e0c317f78438711e0fb6febe70d |
What About Taking Policy as Input of Value Function: Policy-extended Value Function Approximator | 1 INTRODUCTION . Reinforcement learning ( RL ) has been widely considered as a promising way to learn optimal policies in many decision making problems ( Mnih et al. , 2015 ; Lillicrap et al. , 2015 ; Silver et al. , 2016 ; You et al. , 2018 ; Schreck et al. , 2019 ; Vinyals et al. , 2019 ; Hafner et al. , 2020 ) . Lyi... | The paper conditions the value function on a representation of the policy. The representation can be based on a batch of state-action pairs or based on the policy filters. When conditioning on the representation of a new policy, the value function can better approximate the value of the new policy. Experiments show ben... | SP:763cf5ceb0330e0c317f78438711e0fb6febe70d |
Adversarial Data Generation of Multi-category Marked Temporal Point Processes with Sparse, Incomplete, and Small Training Samples | 1 INTRODUCTION . Marked Temporal Point Processes ( MTPPs ) are widely used for modeling and analysis of asynchronous stochastic discrete events in continuous time ( Upadhyay et al. , 2018 ; Türkmen et al. , 2019 ; Yan , 2019 ) with applications in numerous domains such as homeland security , cybersecurity , consumer a... | The authors propose a method for multi-category marked temporal point processes (MTPPs) generation with sparse, incomplete, and small training dataset. They apply Adversarial Autoencoder (AAE) and feature mapping techniques, which include a transformation between the categories and timestamps of marked points and the p... | SP:784bb8350a1f8d1f69734ecaa8395fc4a67b1abf |
Adversarial Data Generation of Multi-category Marked Temporal Point Processes with Sparse, Incomplete, and Small Training Samples | 1 INTRODUCTION . Marked Temporal Point Processes ( MTPPs ) are widely used for modeling and analysis of asynchronous stochastic discrete events in continuous time ( Upadhyay et al. , 2018 ; Türkmen et al. , 2019 ; Yan , 2019 ) with applications in numerous domains such as homeland security , cybersecurity , consumer a... | The paper is concentrated at dealing with the data missing problem of MTPP and applies an AAE for the “incomplete multi-categorical MTPPs”. First, the problem description appears questionable. Point processes are a class of stochastic processes for modelling discrete event sequences in a continuous time domain. They a... | SP:784bb8350a1f8d1f69734ecaa8395fc4a67b1abf |
Maximum Entropy competes with Maximum Likelihood | 1 INTRODUCTION . The maximum entropy ( MAXENT ) method was proposed within statistical physics ( Jaynes , 1957 ; Balian , 2007 ; Pressé et al. , 2013 ) , and later on got a wide range of inter-disciplinary applications in data science , probabilistic inference , biological data modeling etc ; see e.g . ( Erickson & Sm... | This paper investigates maximum entropy (MaxEnt) inference and compares it to a Bayesian estimator and regularized maximum likelihood for finite models. To assess the accuracy of the different estimators, the authors use the average KL-divergence between the ground truth and the estimator, where the average is computed... | SP:f686bbac0c991408e1723e1590faf645d62131bc |
Maximum Entropy competes with Maximum Likelihood | 1 INTRODUCTION . The maximum entropy ( MAXENT ) method was proposed within statistical physics ( Jaynes , 1957 ; Balian , 2007 ; Pressé et al. , 2013 ) , and later on got a wide range of inter-disciplinary applications in data science , probabilistic inference , biological data modeling etc ; see e.g . ( Erickson & Sm... | This paper considers the maximum entropy (MAXENT) method for estimating underlying probabilities over a finite alphabet, i.e., the multinomial model. The authors compare MAXENT with the regularized maximum likelihood, that is the Bayesian estimator under the Dirichlet prior with a common hyperparameter, and the Bayesia... | SP:f686bbac0c991408e1723e1590faf645d62131bc |
Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning | 1 INTRODUCTION . Commonsense knowledge is essential for developing human-level artificial intelligence systems that can understand and interact with the real world . However , commonsense knowledge is assumed by humans and thus rarely written down in text corpora for machines to learn from and make inferences with . Fi... | In this paper, the authors propose a new approach towards incorporating knowledge graphs (KG) into commonsense QA frameworks. KGs are helpful for adding structured "world" information, which neural-symbolic architectures can leverage to do commonsense reasoning, e.g., "what is the expensive resource in printing on pape... | SP:c5bc1d50c01d86f3f5ccb52508cc4112a1937bd4 |
Learning Contextualized Knowledge Graph Structures for Commonsense Reasoning | 1 INTRODUCTION . Commonsense knowledge is essential for developing human-level artificial intelligence systems that can understand and interact with the real world . However , commonsense knowledge is assumed by humans and thus rarely written down in text corpora for machines to learn from and make inferences with . Fi... | The paper proposes a question answering model that is augmented with a common-sense knowledge graph (KG). The paper builds on the following two observations — (a) KGs are incomplete often lacking facts that would be needed for reasoning to answer a question. (b) Current methods over-retrieves facts (edges) from the KG ... | SP:c5bc1d50c01d86f3f5ccb52508cc4112a1937bd4 |
Model-based Navigation in Environments with Novel Layouts Using Abstract $2$-D Maps | Efficiently training agents with planning capabilities has long been one of the major challenges in decision-making . In this work , we focus on zero-shot navigation ability on a given abstract 2-D occupancy map , like human navigation by reading a paper map , by treating it as an image . To learn this ability , we nee... | This paper tackles the task of going to a point-goal using an abstract 2D map of a given environment. The central idea is to use the given abstract 2D map to predict parameters for the transition function in the environment depicted by the map. This predicted transition function is used to search for actions to execute... | SP:4896bf9436d8c03f6751b45118bf29ff0d116c6e |
Model-based Navigation in Environments with Novel Layouts Using Abstract $2$-D Maps | Efficiently training agents with planning capabilities has long been one of the major challenges in decision-making . In this work , we focus on zero-shot navigation ability on a given abstract 2-D occupancy map , like human navigation by reading a paper map , by treating it as an image . To learn this ability , we nee... | This submission tackles the Point Goal navigation task given access to the agent’s starting location, current state (position and velocity), the goal location and a top-down map. The submission presents two approaches for tackling this task. First is MMN (Map-conditioned Multi-task Navigator) which is model-based appro... | SP:4896bf9436d8c03f6751b45118bf29ff0d116c6e |
Contemplating Real-World Object Classification | 1 INTRODUCTION . Object recognition3 can be said to be the most basic problem in vision sciences . It is required in the early stages of visual processing before a system , be it a human or a machine , can accomplish other tasks such as searching , navigating , or grasping . Application of a convolutional neural networ... | This paper revisits ObjectNet dataset closely and found applying classifiers on object bounding box significantly reduces the gap between ImageNet and ObjectNet. The authors further investigates the robustness of CNNs against image perturbations and adversarial attacks, and found limiting the object area to their segme... | SP:c7be5b2c2dbf1bd54ace77553ebab21b9a219ef5 |
Contemplating Real-World Object Classification | 1 INTRODUCTION . Object recognition3 can be said to be the most basic problem in vision sciences . It is required in the early stages of visual processing before a system , be it a human or a machine , can accomplish other tasks such as searching , navigating , or grasping . Application of a convolutional neural networ... | The authors present a follow-up to the prior work of Barbu et al on the task of Object Recognition* (name confusion addressed in cons below). Barbu et al demonstrated that on a more realistic dataset like ObjectNet, models trained on a clean dataset like ImageNet suffer significant degradation. This work reduces the pe... | SP:c7be5b2c2dbf1bd54ace77553ebab21b9a219ef5 |
Retrieval-Augmented Generation for Code Summarization via Hybrid GNN | 1 INTRODUCTION . With software growing in size and complexity , developers tend to spend nearly 90 % ( Wan et al. , 2018 ) effort on software maintenance ( e.g. , version iteration and bug fix ) in the completed life cycle of software development . Source code summary , in the form of natural language , plays a critica... | This paper proposes a retrieval-augmented method for generating code summarization. The model encodes the input code based on its graph structure (Code Property Graph) with a hybrid GNN architecture. The model augments the initial graph representation of the input code based on the representation of the top-1 retrieval... | SP:dc665b731d4611b6164b2f84a031fa8b8d3701da |
Retrieval-Augmented Generation for Code Summarization via Hybrid GNN | 1 INTRODUCTION . With software growing in size and complexity , developers tend to spend nearly 90 % ( Wan et al. , 2018 ) effort on software maintenance ( e.g. , version iteration and bug fix ) in the completed life cycle of software development . Source code summary , in the form of natural language , plays a critica... | This paper leverages similar code-summary pairs from existing data to assist code summary generation. The model first retrieves a similar code snippet from the existing database. Then, the author applied GNN over the code property graphs (CPGs). A challenge is that CPGs are typically deep therefore it is difficult to ... | SP:dc665b731d4611b6164b2f84a031fa8b8d3701da |
Neural Thompson Sampling | 1 INTRODUCTION . The stochastic multi-armed bandit ( Bubeck & Cesa-Bianchi , 2012 ; Lattimore & Szepesvári , 2020 ) has been extensively studied , as an important model to optimize the trade-off between exploration and exploitation in sequential decision making . Among its many variants , the contextual bandit is wide... | The paper proposes neural thompson sampling (TS) - a method to run TS without assuming that the reward is a linear function of the context, as is generally assumed in literature. This is not the first paper to use neural networks for TS, however existing papers either a) used TS only in the last layer, or b) maintained... | SP:e170d43e3733bc0cd7e38f380b63281056dce095 |
Neural Thompson Sampling | 1 INTRODUCTION . The stochastic multi-armed bandit ( Bubeck & Cesa-Bianchi , 2012 ; Lattimore & Szepesvári , 2020 ) has been extensively studied , as an important model to optimize the trade-off between exploration and exploitation in sequential decision making . Among its many variants , the contextual bandit is wide... | The paper proposes a novel Thompson sampling algorithm for neural networks which can be applied to any arbitrary, bounded reward function. While existing works apply Thompson sampling (TS) to neural networks in a heuristic way (e.g., sampling parameters in the last layer only), this algorithm considers the posterior di... | SP:e170d43e3733bc0cd7e38f380b63281056dce095 |
WeMix: How to Better Utilize Data Augmentation | 1 INTRODUCTION . Data augmentation ( Baird , 1992 ; Schmidhuber , 2015 ) has been a key to the success of deep learning in image classification ( He et al. , 2019 ) , and is becoming increasingly common in other tasks such as natural language processing ( Zhang et al. , 2015 ) and object detection ( Zoph et al. , 2019 ... | This paper proposes a method to improve the generalization of deep networks when the input is applied strong augmentations, i.e. mixup, resulting in large data bias. The authors proposes theoretical foundation behind their two methods, AugDrop and MixLoss and show their effectiveness on CIFAR10/CIFAR100 datasets. Altho... | SP:02802f3805d0c51d2a2d3105f7beeb620999bd66 |
WeMix: How to Better Utilize Data Augmentation | 1 INTRODUCTION . Data augmentation ( Baird , 1992 ; Schmidhuber , 2015 ) has been a key to the success of deep learning in image classification ( He et al. , 2019 ) , and is becoming increasingly common in other tasks such as natural language processing ( Zhang et al. , 2015 ) and object detection ( Zoph et al. , 2019 ... | In this work, the authors first prove a deep model can benefit from augmented data when the data bias is small. Then they propose two methods, namely "AugDrop" that corrects and "MixLoss", that correct data bias by "constrained optimisation" and "modified loss function" respectively. Finally they show that these two m... | SP:02802f3805d0c51d2a2d3105f7beeb620999bd66 |
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch | 1 INTRODUCTION . Deep neural networks ( DNNs ) have shown promising performances on various tasks including computer vision , natural language processing , speech recognition , etc . However , a DNN usually comes with a large number of learnable parameters , ranging from millions of to even billions of ( e.g. , GPT-3 (... | The authors proposed a new method for training N:M fine-grained structured sparse networks from scratch. The authors found that the SAD metric, which measures the number of weights whose pruning state is changed, became higher if the existing STE is used to train sparse networks and this metric had the positive relatio... | SP:38a415fd3aa50464470b6deeab96c007364afd17 |
Learning N:M Fine-grained Structured Sparse Neural Networks From Scratch | 1 INTRODUCTION . Deep neural networks ( DNNs ) have shown promising performances on various tasks including computer vision , natural language processing , speech recognition , etc . However , a DNN usually comes with a large number of learnable parameters , ranging from millions of to even billions of ( e.g. , GPT-3 (... | The paper introduces a new sparse training algorithm (SR-STE) based on the straight through estimator which is specially designed for the hardware constraints of Nvidia A100 GPU. Auxiliary, in order to study better this algorithm, the paper introduces also a metric (SAD) to measure the changes in the sparse network top... | SP:38a415fd3aa50464470b6deeab96c007364afd17 |
Spectral Synthesis for Satellite-to-Satellite Translation | 1 INTRODUCTION . Climate change and related environmental issues - including the loss of biodiversity and extreme weather - are listed by the World Economic Forum as the most important risks to our planet ( 7 ) . Monitoring the Earth is critical to mitigating these risks , understanding the effects , and making future ... | This paper tries to generate synthetic unobserved spectral imagery from a set of existing spectral channels. This is an image-to-image translation task, for which VAEs and GANs are effective to address. For this specific satellite band-to-band translation task, authors adopt the VAE-GAN framework (adding a skip connect... | SP:7d69ad75322ea8d15440567c810394321a4f1f34 |
Spectral Synthesis for Satellite-to-Satellite Translation | 1 INTRODUCTION . Climate change and related environmental issues - including the loss of biodiversity and extreme weather - are listed by the World Economic Forum as the most important risks to our planet ( 7 ) . Monitoring the Earth is critical to mitigating these risks , understanding the effects , and making future ... | This paper proposes a new method for image-to-image translation on multi-spectral imagery. The proposed method uses variational auto-encoders and generative adversarial networks to generate synthetic bands in satellite imagery. Novelty of the proposed method is that the authors introduce a shared spectral reconstructi... | SP:7d69ad75322ea8d15440567c810394321a4f1f34 |
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data | 1 INTRODUCTION . The introduction of deep , contextualized Masked Language Models ( MLM ) 1 trained on massive amounts of unlabeled data has led to significant advances across many different Natural Language Processing ( NLP ) tasks ( Peters et al. , 2018 ; Liu et al. , 2019a ) . Much of these recent advances can be at... | This paper proposed a new transformer model framework for multitask learning on NLP tasks. To deal with challenges in multitask learning/co-training, the authors proposed five improvements, including modifications on the transformer layers with task conditioning and uncertainty sampling. In the experiments, the authors... | SP:6a1884e0f3d0e103ad14ab06b3f28308d4ccec2c |
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data | 1 INTRODUCTION . The introduction of deep , contextualized Masked Language Models ( MLM ) 1 trained on massive amounts of unlabeled data has led to significant advances across many different Natural Language Processing ( NLP ) tasks ( Peters et al. , 2018 ; Liu et al. , 2019a ) . Much of these recent advances can be at... | The paper explores a collection of strategies to improve multitask learning and bring performance on par with single task training. The strategies build on and show good awareness of existing work and achieve positive overall results on GLUE, SuperGLUE, and MRQA tasks. Abolation experiments demonstrate the value of the... | SP:6a1884e0f3d0e103ad14ab06b3f28308d4ccec2c |
A straightforward line search approach on the expected empirical loss for stochastic deep learning problems | 1 INTRODUCTION AND BACKGROUND . The automatic determination of an optimal learning rate schedule to train models with stochastic gradient descent or similar optimizers is still not solved satisfactorily for standard and especially new deep learning tasks . Frequently , optimization approaches utilize the information of... | The paper tackles an important issue, namely how to tune the step sizes in SGD during the training, trying to approximate step sizes which would be used in GD (even though the search direction is still noisy). Relevant prior work is cited. The method is simple and easy to understand. Step sizes are kept piecewise const... | SP:911e9288b3fc37197681962df1df1db17fcffd52 |
A straightforward line search approach on the expected empirical loss for stochastic deep learning problems | 1 INTRODUCTION AND BACKGROUND . The automatic determination of an optimal learning rate schedule to train models with stochastic gradient descent or similar optimizers is still not solved satisfactorily for standard and especially new deep learning tasks . Frequently , optimization approaches utilize the information of... | This work proposes ELF, a newl method to do line search. The key idea is to fit a low order polynomial of the empirical loss (by sampling multiple batches) along the direction of a mini-batch gradient. The method stays computationally efficient by only computing the step size every so often. Experiments on a variety of... | SP:911e9288b3fc37197681962df1df1db17fcffd52 |
Extracting Strong Policies for Robotics Tasks from Zero-Order Trajectory Optimizers | 1 INTRODUCTION . The general purpose of model-based and model-free reinforcement learning ( RL ) is to optimize a trajectory or find a policy that is fast and accurate enough to be deployed on real robotic systems . Policies optimized by model-free RL algorithms achieve outstanding results for many challenging domains ... | This paper presents an approach to distill a model-based planning expert into a policy to enable real-time execution on robotic systems. An improved version of the Cross-Entropy Method, iCEM is used to generate trajectories via model-based optimisation where the forward model is the ground-truth simulator dynamics. The... | SP:168f23644e133a08f2e16ee31b0a8f0620aea96d |
Extracting Strong Policies for Robotics Tasks from Zero-Order Trajectory Optimizers | 1 INTRODUCTION . The general purpose of model-based and model-free reinforcement learning ( RL ) is to optimize a trajectory or find a policy that is fast and accurate enough to be deployed on real robotic systems . Policies optimized by model-free RL algorithms achieve outstanding results for many challenging domains ... | In summary, this paper combines GPS and DAgger to learn a policy network by imitating a model-based controller, which uses iCEM as the optimizer. Their approach uses both the iCEM controller and the learned policy with DAgger-like relabeling to collect data and then train the network with behavior cloning. An auxiliary... | SP:168f23644e133a08f2e16ee31b0a8f0620aea96d |
Learning Safe Multi-agent Control with Decentralized Neural Barrier Certificates | 1 INTRODUCTION . Machine learning ( ML ) has created unprecedented opportunities for achieving full autonomy . However , learning-based methods in autonomous systems ( AS ) can and do fail due to the lack of formal guarantees and limited generalization capability , which poses significant challenges for developing safe... | This paper extends the neural barrier certificate method from reinforcement learning to the decentralized multi-agent reinforcement learning setting. In particular, it borrows the idea of the control barrier function, which enforces the states of the dynamic system to stay in the safe set. It is known that in the sin... | SP:e01cbc55e8f7bc90ed66b50387234c154f547e5e |
Learning Safe Multi-agent Control with Decentralized Neural Barrier Certificates | 1 INTRODUCTION . Machine learning ( ML ) has created unprecedented opportunities for achieving full autonomy . However , learning-based methods in autonomous systems ( AS ) can and do fail due to the lack of formal guarantees and limited generalization capability , which poses significant challenges for developing safe... | This paper brings the recently introduced idea of control barrier functions (CBF) as safety certificate to the multi-agent land. To this end it introduces the idea of decentralized CBFs. This is followed with a framework for jointy learning the CBFs and policies with a PointNet inspired network architecture. The paper ... | SP:e01cbc55e8f7bc90ed66b50387234c154f547e5e |
Two steps at a time --- taking GAN training in stride with Tseng's method | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) have proven to be a powerful class of generative models , producing for example unseen realistic images . Two neural networks , called generator and discriminator , compete against each other in a game . In the special case of a zero... | The authors in this paper, inspired by the applications of min-max optimization in GANs, study the problem of min-max optimization for convex-concave functions. The main contribution of the paper is proving novel convergence results for Forward-Backward-Forward (FBF) algorithms as well as Optimistic Gradient Descent As... | SP:84d3ab8eb02111204e89559450fe3062212bcc9d |
Two steps at a time --- taking GAN training in stride with Tseng's method | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) have proven to be a powerful class of generative models , producing for example unseen realistic images . Two neural networks , called generator and discriminator , compete against each other in a game . In the special case of a zero... | This work studies minimax optimization (a.k.a saddle-point problems) with nonsmooth regularizers. By leveraging the monotone operator theory, the authors propose to use the forward-backward-forward method so as to avoids the notorious limit cycling problem. The classical FBF method requires two gradient evaluations per... | SP:84d3ab8eb02111204e89559450fe3062212bcc9d |
Convolutional Neural Networks are not invariant to translation, but they can learn to be | 1 INTRODUCTION . The equivalence of an objects across different viewpoints is considered a fundamental capacity of human vision recognition ( Hummel , 2002 ) . This is mediated by the inferior temporal cortex , which appears to provide the bases for scale , translation , and rotation invariance ( Tanaka , 1996 ; O ’ Re... | This paper analysis and studies translation invariance in convolution neural networks. It argues that typically it is claimed that CNNs are translation invariant due to the convolution function, and that actually convolution are equivariant. While pooling is the actual function that gives local invariance (or global wh... | SP:d038f28f90bdf4625c61eefe75dd062ebf583fc8 |
Convolutional Neural Networks are not invariant to translation, but they can learn to be | 1 INTRODUCTION . The equivalence of an objects across different viewpoints is considered a fundamental capacity of human vision recognition ( Hummel , 2002 ) . This is mediated by the inferior temporal cortex , which appears to provide the bases for scale , translation , and rotation invariance ( Tanaka , 1996 ; O ’ Re... | This paper addresses the problem of how convolutional neural networks (CNNs) achieve translation invariance, and the authors argue that this invariance es mostly learned from suitable datasets, rather than a result of the architecture. In particular, ImageNet-pretrained networks have learned to be invariant to translat... | SP:d038f28f90bdf4625c61eefe75dd062ebf583fc8 |
Is Attention Better Than Matrix Decomposition? | 1 INTRODUCTION . Since self-attention and transformer ( Vaswani et al. , 2017 ) showed significant advantages over recurrent neural networks and convolutional neural networks in capturing long-distance dependencies , attention has been widely adopted by computer vision ( Wang et al. , 2018 ; Zhang et al. , 2019a ) and ... | The paper presents a method based on matrix decomposition (MD) for encoding global context in computer vision tasks. In particular, a "Hamburger" block is proposed encompassing matrix decomposition as its central part, between two linear projection layers. Direct comparison and relations are drawn between the proposed ... | SP:ea04662e871c7eef151c4c7b61464453f391ed9f |
Is Attention Better Than Matrix Decomposition? | 1 INTRODUCTION . Since self-attention and transformer ( Vaswani et al. , 2017 ) showed significant advantages over recurrent neural networks and convolutional neural networks in capturing long-distance dependencies , attention has been widely adopted by computer vision ( Wang et al. , 2018 ; Zhang et al. , 2019a ) and ... | This paper proposes to use matrix decomposition to construct low-rank representations to find the long-distance correlations in context, which is demonstrated more effective than popular self-attention mechanism. Combining linear transformation and matrix decomposition (core part), authors design Hamburger block to mod... | SP:ea04662e871c7eef151c4c7b61464453f391ed9f |
ColdExpand: Semi-Supervised Graph Learning in Cold Start | 1 INTRODUCTION . Graph-based semi-supervised learning has attracted much attention thanks to its applicability to real-world problems . For example , a social network is graph-structured data in which people in the network are considered to be nodes and relationships between people are considered to be edges : two peop... | This paper is about the cold-start problem for representation learning on dynamics graphs. More specifically, the proposed method (ColdExpand) uses convolutional networks and multi-task learning (node classification loss and link prediction loss) to learn embeddings for new unseen nodes in the graph, i.e., nodes that w... | SP:0365e3071ba7036ff25566c543c65cfc92531674 |
ColdExpand: Semi-Supervised Graph Learning in Cold Start | 1 INTRODUCTION . Graph-based semi-supervised learning has attracted much attention thanks to its applicability to real-world problems . For example , a social network is graph-structured data in which people in the network are considered to be nodes and relationships between people are considered to be edges : two peop... | The paper proposes a new task in Graph Learning. Basically, the idea is the following: suppose we have a node classification model trained on a Graph G, suppose we have a new node (not present in G) and we want to classify it. Given that the new node has no connections with G’s other nodes we cannot leverage any struct... | SP:0365e3071ba7036ff25566c543c65cfc92531674 |
Multi-Agent Imitation Learning with Copulas | 1 INTRODUCTION . Recent years have witnessed great success of reinforcement learning ( RL ) for single-agent sequential decision making tasks . As many real-world applications ( e.g. , multi-player games ( Silver et al. , 2017 ; Brown & Sandholm , 2019 ) and traffic light control ( Chu et al. , 2019 ) ) involve the par... | The paper proposes the use of "Copulas" to capture dependencies among agents in multi-player environments. The authors argue that prior work(eg GNN, VAE, LSTM etc based) do not leverage the common structure among agent behaviour. They explain how the copula can more efficiently encode the dependency among policies, as ... | SP:900ada28e9a27c6ad856871ad5f04c50dc95e023 |
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