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Average-case Acceleration for Bilinear Games and Normal Matrices | 1 INTRODUCTION . The traditional analysis of optimization algorithms is a worst-case analysis ( Nemirovski , 1995 ; Nesterov , 2004 ) . This type of analysis provides a complexity bound for any input from a function class , no matter how unlikely . However , since hard-to-solve inputs might rarely occur in practice , t... | The paper combines two recent areas of interest in optimization for machine learning: average-case analysis for acceleration and learning in bilinear games. Average-case optimal methods are proposed for some distributions on eigenvalues for the Jacobian of the games vector-field. Also, a connection is made between ave... | SP:ff34a84b45570d684598dda4a9cd63be2a459e51 |
On Single-environment Extrapolations in Graph Classification and Regression Tasks | 1 INTRODUCTION . In some graph classification and regression applications , the graphs themselves are representations of a natural process rather than the true state of the process . Molecular graphs are built from a pairwise atom distance matrix by keeping edges whose distance is below a certain threshold and the choi... | This paper formulates a theoretical model for testing extrapolation abilities of graph learning tasks, and suggests some practical feature maps to achieve good extrapolation properties empirically. In more detail, this paper introduces a model, a so-called structural causal model, for graphs where the graph creation pr... | SP:6bb786b66e6692c476fc201d325e82f4fc2e15b8 |
On Single-environment Extrapolations in Graph Classification and Regression Tasks | 1 INTRODUCTION . In some graph classification and regression applications , the graphs themselves are representations of a natural process rather than the true state of the process . Molecular graphs are built from a pairwise atom distance matrix by keeping edges whose distance is below a certain threshold and the choi... | The paper explores the problem of extrapolation in graph classification tasks and by leveraging Lovasz’s graph limit theory, provides graph representations and related theoretical guarantees on graph size extrapolation in the context of unattributed graphs. Specifically, it is shown that the graph representations chara... | SP:6bb786b66e6692c476fc201d325e82f4fc2e15b8 |
Dynamically Stable Infinite-Width Limits of Neural Classifiers | 1 INTRODUCTION . For a couple of decades neural networks have proved to be useful in a variety of applications . However , their theoretical understanding is still lacking . Several recent works have tried to simplify the object of study by approximating a training dynamics of a finite-width neural network with its lim... | The paper analyzes joint scalings of the parameter initialization and the learning rate, with respect to the limit of infinite width, in the context of two-layer neural networks with stochastic gradient descent and binary logistic loss. It proposes some “dynamically stable” conditions and identifies a range of scalings... | SP:df86cedbf3a09f5d07380bcc99ee1674758a309b |
Dynamically Stable Infinite-Width Limits of Neural Classifiers | 1 INTRODUCTION . For a couple of decades neural networks have proved to be useful in a variety of applications . However , their theoretical understanding is still lacking . Several recent works have tried to simplify the object of study by approximating a training dynamics of a finite-width neural network with its lim... | This paper proposed a general framework to derive different stable limiting behaviors of the dynamics of two-layers neural networks, under different parameterization of the hyper-parameters. For certain choices of hyper-parameters, this recovers the mean-field limit and the NTK limit. This paper also proposed certain p... | SP:df86cedbf3a09f5d07380bcc99ee1674758a309b |
Alpha Net: Adaptation with Composition in Classifier Space | Deep learning classification models typically train poorly on classes with small numbers of examples . Motivated by the human ability to solve this task , models have been developed that transfer knowledge from classes with many examples to learn classes with few examples . Critically , the majority of these models tra... | This paper addresses the well-known long-tail classification problem. The argument made here is that most of the existing methods attempt to transfer knowledge in the feature space, which is true. Based on this motivation, the paper proposes a method to do the knowledge transfer in the model space instead. The idea is ... | SP:e8386ff9f69f7ffae3bf7fe122d61ef7b81fd425 |
Alpha Net: Adaptation with Composition in Classifier Space | Deep learning classification models typically train poorly on classes with small numbers of examples . Motivated by the human ability to solve this task , models have been developed that transfer knowledge from classes with many examples to learn classes with few examples . Critically , the majority of these models tra... | This paper focuses on how to transfer knowledge between classes. The authors proposed to transfer classifiers instead of features. The proposed to linearly combine the classifiers from rich classes to construct more robust classifiers or rare classes. The combination weights are predicted from a learned neural network ... | SP:e8386ff9f69f7ffae3bf7fe122d61ef7b81fd425 |
Are Graph Convolutional Networks Fully Exploiting the Graph Structure? | Graph Convolutional Networks ( GCNs ) represent the state-of-the-art for many graph related tasks . At every layer , GCNs rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours . A known limitation of GCNs is their inability ... | This paper proposes to use k-step RWR to capture long-range structural information as a part of node features, so as to overcome the limitation of GCN that it cannot infer long-range dependencies. However, the methodology is incremental and cannot easily be applied to real-world large-scale networks. In summary, the or... | SP:8971d18110014de85c721169795edb6c101a3706 |
Are Graph Convolutional Networks Fully Exploiting the Graph Structure? | Graph Convolutional Networks ( GCNs ) represent the state-of-the-art for many graph related tasks . At every layer , GCNs rely on the graph structure to define an aggregation strategy where each node updates its representation by combining information from its neighbours . A known limitation of GCNs is their inability ... | This paper studies GCNs when long-range dependencies have been added to the model as a regularizer. The regularizer proposed in this work is based on a random walk with restart (RWR) approach as RWR encourages the model to consider long-range dependencies. This paper shows that infusing the long-range dependencies usin... | SP:8971d18110014de85c721169795edb6c101a3706 |
Neurally Augmented ALISTA | 1 INTRODUCTION AND RELATED WORK . Compressed sensing deals with the problem of recovering a sparse vector from very few compressive linear observations , far less than its ambient dimension . Fundamental works of Candes et al . ( Candès et al. , 2006 ) and Donoho ( Donoho , 2006 ) show that this can be achieved in a ro... | The paper at hand introduces Neurally augmented ALISTA (NA-ALISTA) which is an extension to the previously proposed analytical learned iterative shrinkage threshold algorithm (ALISTA). Both algorithms belong to the class of learned optimization algorithms for solving the compressed sensing problem, i.e., methods that h... | SP:7883aef70e8fefe38482cd2baeb645e94b21c4d2 |
Neurally Augmented ALISTA | 1 INTRODUCTION AND RELATED WORK . Compressed sensing deals with the problem of recovering a sparse vector from very few compressive linear observations , far less than its ambient dimension . Fundamental works of Candes et al . ( Candès et al. , 2006 ) and Donoho ( Donoho , 2006 ) show that this can be achieved in a ro... | This paper extends the framework of ALISTA, a variant of learned ISTA called Neurally Augmented ALISTA (AG-ALISTA), which significantly reduces the number parameters in the model (down to 2 scalars per layer, one for step size and the other for the threshold in soft-thresholding function). Specifically, the authors use... | SP:7883aef70e8fefe38482cd2baeb645e94b21c4d2 |
Unsupervised Active Pre-Training for Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( RL ) provides a general framework for solving challenging sequential decision-making problems , it has achieved remarkable success in advancing the frontier of AI technologies thanks to scalable and efficient learning algorithms ( Mnih et al. , 2015 ; Lillicrap et al. , 20... | The paper proposes a method to simultaneously learn effective representations and efficient exploration in a reward-free context. The algorithm iterates between minimizing a contrastive loss and maximizing an intrinsic reward derived from a k-NN entropy estimation of the state distribution. Then, authors empirically ev... | SP:77b114448647f9b8edcd6dad7d8be6e152e44696 |
Unsupervised Active Pre-Training for Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( RL ) provides a general framework for solving challenging sequential decision-making problems , it has achieved remarkable success in advancing the frontier of AI technologies thanks to scalable and efficient learning algorithms ( Mnih et al. , 2015 ; Lillicrap et al. , 20... | This submission presents a technique for unsupervised pre-training of representations and policies for RL. Unsupervised representation learning has obtained impressive results in supervised scenarios, and adapting these methods to RL is an important research direction. One of the main challenges in the RL setting is th... | SP:77b114448647f9b8edcd6dad7d8be6e152e44696 |
Uniform Priors for Data-Efficient Transfer | 1 INTRODUCTION . Deep Neural Networks have enabled great success in various machine learning domains such as computer vision ( Girshick , 2015 ; He et al. , 2016 ; Long et al. , 2015 ) , natural language processing ( Vaswani et al. , 2017 ; Devlin et al. , 2018 ; Brown et al. , 2020 ) , decision making ( Schulman et al... | In this paper, the authors claimed that uniformity in embedding space if the key for good generalization, and then propose an adversarial training based method to improve the uniformity of feature space. The claim is from previous work, thus the key contribution is the way to impose such regularization. The method itse... | SP:73bf13710be8ac67ecba0e706e128b807d9d64a5 |
Uniform Priors for Data-Efficient Transfer | 1 INTRODUCTION . Deep Neural Networks have enabled great success in various machine learning domains such as computer vision ( Girshick , 2015 ; He et al. , 2016 ; Long et al. , 2015 ) , natural language processing ( Vaswani et al. , 2017 ; Devlin et al. , 2018 ; Brown et al. , 2020 ) , decision making ( Schulman et al... | The authors argue that uniform priors for the high-level latent representations improve transferability, which is beneficial in a number of tasks involving transference. The approach is evaluated on deep metric learning, zero-shot domain adaptation and few-shot meta-learning. The authors propose a uniformity regulariza... | SP:73bf13710be8ac67ecba0e706e128b807d9d64a5 |
Uncertainty Weighted Offline Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has seen a surge of interest over the recent years . It has achieved remarkable success in simulated tasks ( Silver et al. , 2017 ; Schulman et al. , 2017 ; Haarnoja et al. , 2018 ) , where the cost of data collection is low . However , one of the drawbacks of RL is i... | This paper proposes to use an uncertainty-weighted objective for offline RL with BEAR (Kumar et al.) that penalizes the MMD distance between the learned policy and the previous policy. The uncertainty weighted objective weights the policy improvement objective with the variance in the Q-function, where this variance pr... | SP:650d7f17c93a91cce9f886766671ed19044deecd |
Uncertainty Weighted Offline Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has seen a surge of interest over the recent years . It has achieved remarkable success in simulated tasks ( Silver et al. , 2017 ; Schulman et al. , 2017 ; Haarnoja et al. , 2018 ) , where the cost of data collection is low . However , one of the drawbacks of RL is i... | This paper considers the problem of dealing with uncertainty for static datasets in offlineRL. The authors propose a novel algorithm ‘UWAC’, uncertainty weighted actor-critic. UWAC takes a Bayesian perspective of RL, and uses Monte Carlo dropout for detecting, down-weighting OOD samples. Building on BEAR, they estimate... | SP:650d7f17c93a91cce9f886766671ed19044deecd |
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting | 1 INTRODUCTION . The field of graph representation learning has undergone a rapid growth in the past few years . In particular , Graph Neural Networks ( GNNs ) , a family of neural architectures designed for irregularly structured data , have been successfully applied to problems ranging from social networks and recomm... | This paper presents a natural extension of Message Passing Neural Net (MPNN) by incorporating structural features. These structural features are computed as the counts from different substructures (like small lines, stars or complete graphs) induced in the original graph. These counts are combined to obtain a new feat... | SP:d6b0ad3bacba7ba1a532470056cd180ec9bf9688 |
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting | 1 INTRODUCTION . The field of graph representation learning has undergone a rapid growth in the past few years . In particular , Graph Neural Networks ( GNNs ) , a family of neural architectures designed for irregularly structured data , have been successfully applied to problems ranging from social networks and recomm... | This work proposes the Graph Substructure Network (GSN) to encode structural roles for different nodes so that the expressivity of Graph Neural Networks is improved. The core idea is to count the number of certain substructures, such as cycles, cliques, and triangles. Then the proposed MPNN encodes such substructure co... | SP:d6b0ad3bacba7ba1a532470056cd180ec9bf9688 |
Efficiently Disentangle Causal Representations | 1 INTRODUCTION . Causal reasoning is a fundamental tool that has shown great impact in different disciplines ( Rubin & Waterman , 2006 ; Ramsey et al. , 2010 ; Rotmensch et al. , 2017 ) , and it has roots in work by David Hume in the eighteenth century ( Hume , 2003 ) and in classical AI ( Pearl , 2003 ) . Causality ha... | This paper describes an approach for learning a representation U(X,Y), V(X,Y) of data (X,Y) such that U and V are causally meaningful, and U causes V. The approach relies on observing data from two domains, P and Q, where P(V| U) = Q(V |U) (reflecting the causal structure). The approach is a modification of Bengio et a... | SP:c0d72e20146d4608068bd607c0d1fe306e821779 |
Efficiently Disentangle Causal Representations | 1 INTRODUCTION . Causal reasoning is a fundamental tool that has shown great impact in different disciplines ( Rubin & Waterman , 2006 ; Ramsey et al. , 2010 ; Rotmensch et al. , 2017 ) , and it has roots in work by David Hume in the eighteenth century ( Hume , 2003 ) and in classical AI ( Pearl , 2003 ) . Causality ha... | The paper presents a novel approach for determining the causal direction between two random variables $A$ and $B$. The approach is based on the assumption that the conditional distribution $P_{A \rightarrow B}(B \mid B)$ does not change between the train and transfer distribution. As a result, a model that predicts the... | SP:c0d72e20146d4608068bd607c0d1fe306e821779 |
Learning Parametrised Graph Shift Operators | 1 INTRODUCTION . Real-world data and applications often involve significant structural complexity and as a consequence graph representation learning attracts great research interest ( Hamilton et al. , 2017b ; Wu et al. , 2020 ) . The topology of the observations plays a central role when performing machine learning ta... | The paper proposes a parametric form for a matrix representation of a graph to be used as a building block within graph neural networks (GNNs). In essence, people use different normalized versions of the adjacency and Laplacian matrices within GNNs. The authors, in turn, propose a generic parametrized version that enco... | SP:424eaac0eb5a5fcb526c39f5e21f9ec7506aa3bc |
Learning Parametrised Graph Shift Operators | 1 INTRODUCTION . Real-world data and applications often involve significant structural complexity and as a consequence graph representation learning attracts great research interest ( Hamilton et al. , 2017b ; Wu et al. , 2020 ) . The topology of the observations plays a central role when performing machine learning ta... | The authors consider the problem of learning a parametrized graph shift operator (or message passing operator) in the context of graph neural networks. They consider a family of GSO (that they name PGSO) based on seven scalar parameters, and show that it includes most commonly used operators such as the adjacency matri... | SP:424eaac0eb5a5fcb526c39f5e21f9ec7506aa3bc |
Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has achieved superior performance in several domains such as games ( Mnih et al. , 2013 ; Silver et al. , 2016 ) and robotic control ( Levine et al. , 2016 ; Rajeswaran et al. , 2017 ) . However , in these complex applications , learning policies from scratch often re... | The paper considers the constrained Markov Decision Process (CMDP) problem where the goal is to maximize cumulative reward while satisfying the safety constraint on cumulative cost. Solving CMDP is challenging, and this paper proposes a shortcut by utilizing a given baseline policy. The idea of the proposed SPACE algor... | SP:a6dfcd6335c24f1ee193abb860a5c67048be8c03 |
Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has achieved superior performance in several domains such as games ( Mnih et al. , 2013 ; Silver et al. , 2016 ) and robotic control ( Levine et al. , 2016 ; Rajeswaran et al. , 2017 ) . However , in these complex applications , learning policies from scratch often re... | The authors propose SPACE, an RL algorithm for learning a policy that maximizes reward while satisfying given constraints in a setting where a baseline policy is provided. They design a three-step update rule for learning such a policy and provide a finite-sample analysis of the resulting method in a simplified setting... | SP:a6dfcd6335c24f1ee193abb860a5c67048be8c03 |
Not All Memories are Created Equal: Learning to Expire | 1 INTRODUCTION . Transformer architectures ( Vaswani et al. , 2017 ) have demonstrated strong performance across a variety of tasks ( Devlin et al. , 2019 ; Roller et al. , 2020 ; Brown et al. , 2020 ) , including those that require learning long term relationships ( Zhang et al. , 2018 ; Fan et al. , 2019a ; Izacard &... | The paper proposes a method for overcoming the long-term memory bottleneck of transformers. The idea is to assign a value (expire-span) to each formed memory, which indicates how long the memory should be stored and be available for the transformer to access it. The authors demonstrate the performance of their approach... | SP:0228dd243b9ca1b38e728d54f5d000858f959ea4 |
Not All Memories are Created Equal: Learning to Expire | 1 INTRODUCTION . Transformer architectures ( Vaswani et al. , 2017 ) have demonstrated strong performance across a variety of tasks ( Devlin et al. , 2019 ; Roller et al. , 2020 ; Brown et al. , 2020 ) , including those that require learning long term relationships ( Zhang et al. , 2018 ; Fan et al. , 2019a ; Izacard &... | To help Transformer learn long sequence efficiently, the paper performs attention on selective timesteps that have high expire-span scores. For each timestep, the expire-span score is computed by mapping the corresponding hidden feature to a number, which is learnt during training. Soft masking is applied to make the l... | SP:0228dd243b9ca1b38e728d54f5d000858f959ea4 |
Quantifying Exposure Bias for Open-ended Language Generation | 1 INTRODUCTION . Language model ( LM ) is a central module for natural language generation ( NLG ) tasks ( Young et al. , 2017 ) such as open-ended language generation ( Radford et al. , 2018 ; Nadeem et al. , 2020 ) , machine translation ( Wu et al. , 2017 ) , dialogue response generation ( Li et al. , 2017 ) , image ... | This paper makes a key observation: “exposure bias” is blamed for many of the issues with Neural Language Generation but it lacks both a concrete definition or any obvious evidence that it truly exists. The authors begin by defining exposure bias as the decrease in quality and relevancy (to the conditioning text) in ge... | SP:a0fa3a2620b05676185a997067aa3e514cd56a35 |
Quantifying Exposure Bias for Open-ended Language Generation | 1 INTRODUCTION . Language model ( LM ) is a central module for natural language generation ( NLG ) tasks ( Young et al. , 2017 ) such as open-ended language generation ( Radford et al. , 2018 ; Nadeem et al. , 2020 ) , machine translation ( Wu et al. , 2017 ) , dialogue response generation ( Li et al. , 2017 ) , image ... | The paper studies the exposure bias in auto-regressive neural language models. This problem is known to cause incremental performance degradation, and attempts to mitigate this problem have received significant attention in the community (using, e.g., RL and GANs). The paper claims that prior work has mostly focused on... | SP:a0fa3a2620b05676185a997067aa3e514cd56a35 |
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime | 1 INTRODUCTION . Recent studies have revealed why a stochastic gradient descent for neural networks converges to a global minimum and why it generalizes well under the overparameterized setting in which the number of parameters is larger than the number of given training examples . One prominent approach is to map the ... | This paper considers the optimization of a wide two layers neural network (for a regression task) using averaged SGD. The authors consider the Neural Tangent Kernel (NTK) regime. The NTK is a kernel defined using the activation function and the initial distribution of the parameters of the input layer. The RKHS H assoc... | SP:ccdb108ba692c4227be899e453faf3c36102ff71 |
Optimal Rates for Averaged Stochastic Gradient Descent under Neural Tangent Kernel Regime | 1 INTRODUCTION . Recent studies have revealed why a stochastic gradient descent for neural networks converges to a global minimum and why it generalizes well under the overparameterized setting in which the number of parameters is larger than the number of given training examples . One prominent approach is to map the ... | This paper analyzed the averaged SGD for overparameterized two-layer NNs for regression problems. Particularly, they show that the averaged SGD can achieve the minimax optimal convergence rate, with the global convergence guarantee. To achieve, they propose a new parameter which captures the ``complexities’’ of the tar... | SP:ccdb108ba692c4227be899e453faf3c36102ff71 |
TropEx: An Algorithm for Extracting Linear Terms in Deep Neural Networks | 1 INTRODUCTION . Many of the most widely used neural network architectures , including VGG ( Simonyan & Zisserman , 2015 ) , GoogLeNet ( Szegedy et al. , 2015 ) and ResNet ( He et al. , 2016 ) , make use of rectified linear activations ( ReLU , ( Hahnloser et al. , 2000 ; Glorot et al. , 2011 ) , i.e. , σ ( x ) = max {... | This paper studies the role of linear terms in the network performance using nontrivial tropical algebra inspired algorithms. In particular, the paper extracts linear terms associated with the linear regions of only the training points, and uses this to generate an extracted network function. The paper proposes an algo... | SP:c42bf54b98bda516112c7b604dd2a84002c4ecfa |
TropEx: An Algorithm for Extracting Linear Terms in Deep Neural Networks | 1 INTRODUCTION . Many of the most widely used neural network architectures , including VGG ( Simonyan & Zisserman , 2015 ) , GoogLeNet ( Szegedy et al. , 2015 ) and ResNet ( He et al. , 2016 ) , make use of rectified linear activations ( ReLU , ( Hahnloser et al. , 2000 ; Glorot et al. , 2011 ) , i.e. , σ ( x ) = max {... | The paper proposes a method for representing a computation of a neural network over a finite set of inputs by a projection onto a set of linear transformations (from the input space) called tropical rational map (TRM). TRM is essentially a snapshot representing the internal representation of however deep network based... | SP:c42bf54b98bda516112c7b604dd2a84002c4ecfa |
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval | 1 INTRODUCTION . Many language systems rely on text retrieval as their first step to find relevant information . For example , search ranking ( Nogueira & Cho , 2019 ) , open domain question answering ( OpenQA ) ( Chen et al. , 2017 ) , and fact verification ( Thorne et al. , 2018 ) all first retrieve relevant document... | This paper studies the problem of dense text retrieval, which represents texts as dense vectors for approximate nearest neighbors (ANN) search. Dense text retrieval has two phases. The first phase learns a representation model to project semantically similar texts to vectors of large similarity scores (e.g. inner produ... | SP:fe103976fa70a0c45fbc1056c39851a76a3e1451 |
Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval | 1 INTRODUCTION . Many language systems rely on text retrieval as their first step to find relevant information . For example , search ranking ( Nogueira & Cho , 2019 ) , open domain question answering ( OpenQA ) ( Chen et al. , 2017 ) , and fact verification ( Thorne et al. , 2018 ) all first retrieve relevant document... | Authors start from an assumption: “local negative sampling is the bottleneck of dense retrieval’s effectiveness”. To overcome this limitation, authors propose ANCE (Approximate nearest neighbour Negative Contrastive Estimation), a new contrastive representation learning mechanism for dense retrieval. The basic idea is ... | SP:fe103976fa70a0c45fbc1056c39851a76a3e1451 |
Unsupervised Cross-lingual Representation Learning for Speech Recognition | 1 INTRODUCTION . Cross-lingual learning aims to build models which leverage data from other languages to improve performance . This has been a long standing interest in the speech community ( Byrne et al. , 2000 ; Le & Besacier , 2009 ; Ghoshal et al. , 2013 ; Huang et al. , 2013 ; Gales et al. , 2017 ; Cho et al. , 20... | Authors extended the XLSR model from the previous mono-lingual task, where self-supervised learning was used in representation learning, to multi-lingual task. The basic idea does make a lot of sense, where all the data, irrespective of the language, is pushed through the representation learning task. We are anyways t... | SP:367eefa594e6815c8a09c675fcf89f2fe29afd5d |
Unsupervised Cross-lingual Representation Learning for Speech Recognition | 1 INTRODUCTION . Cross-lingual learning aims to build models which leverage data from other languages to improve performance . This has been a long standing interest in the speech community ( Byrne et al. , 2000 ; Le & Besacier , 2009 ; Ghoshal et al. , 2013 ; Huang et al. , 2013 ; Gales et al. , 2017 ; Cho et al. , 20... | This paper proposes an unsupervised cross-lingual speech representation learning algorithm for multi-lingual automatic speech recognition. The main building block is based on an existing study (wav2vec series). The idea of this study is to apply previously (successful) unsupervised speech representation learning scheme... | SP:367eefa594e6815c8a09c675fcf89f2fe29afd5d |
Privacy-preserving Learning via Deep Net Pruning | Neural network pruning has demonstrated its success in significantly improving the computational e ciency of deep models while only introducing a small reduction on final accuracy . In this paper , we explore an extra bonus of neural network pruning in terms of enhancing privacy . Specifically , we show a novel connect... | Overview: This paper aims to establish a theoretical connection between differential privacy and magnitude based pruning. The authors show theoretically that outputs of pruned single layer neural networks have some similarity to outputs of the same network with differential privacy added. The paper then empirically dem... | SP:859e6be6784f83c885d384954c106b3e9f1bf39f |
Privacy-preserving Learning via Deep Net Pruning | Neural network pruning has demonstrated its success in significantly improving the computational e ciency of deep models while only introducing a small reduction on final accuracy . In this paper , we explore an extra bonus of neural network pruning in terms of enhancing privacy . Specifically , we show a novel connect... | The paper claims to exhibit a connection between differential privacy and neural network pruning. The main result is that, for a single layer network with Gaussian weights that depend on the privacy parameters, there exists a differentially private function which approximates the pruned network in $L_2$ distance. This ... | SP:859e6be6784f83c885d384954c106b3e9f1bf39f |
Pea-KD: Parameter-efficient and accurate Knowledge Distillation | 1 INTRODUCTION . How can we improve the accuracy of knowledge distillation ( KD ) with smaller number of parameters ? KD uses a well-trained large teacher model to train a smaller student model . Conventional KD method ( Hinton et al . ( 2006 ) ) trains the student model using the teacher model ’ s predictions as targe... | This paper proposes a distillation method for BERT. The work is based on two-fold main ideas. First, as the student model is usually smaller in the number of parameters, the model capacity is limited. The authors propose to stack the layers that share parameters to counter this limitation. Second, the authors argue tha... | SP:1c0bc1ab70b7607f52d991c48ddad1c6b998a702 |
Pea-KD: Parameter-efficient and accurate Knowledge Distillation | 1 INTRODUCTION . How can we improve the accuracy of knowledge distillation ( KD ) with smaller number of parameters ? KD uses a well-trained large teacher model to train a smaller student model . Conventional KD method ( Hinton et al . ( 2006 ) ) trains the student model using the teacher model ’ s predictions as targe... | This paper proposed a framework for knowledge distillation with smaller number of parameters.The authors proposed a new parameter sharing method that allows a greater model complexity for the student model. Another contribution is that a KD-specialized initialization method named Pretraining with Teacher’s Predictions ... | SP:1c0bc1ab70b7607f52d991c48ddad1c6b998a702 |
Human-interpretable model explainability on high-dimensional data | 1 INTRODUCTION . The explainability of AI systems is important , both for model development and model assurance . This importance continues to rise as AI models – and the data on which they are trained – become ever more complex . Moreover , methods for AI explainability must be adapted to maintain the human-interpreta... | The paper proposes an approach to generate semantic explanations for high-dimensional data. The proposed approach consists of two modules -- the first module transforms the high-dimensional raw data into lower-dimensional semantic latent space and the second module applies Shapely explainability to this lower-dimension... | SP:44a4a56c6048b2e7deb16dc106f4552e8f2227b9 |
Human-interpretable model explainability on high-dimensional data | 1 INTRODUCTION . The explainability of AI systems is important , both for model development and model assurance . This importance continues to rise as AI models – and the data on which they are trained – become ever more complex . Moreover , methods for AI explainability must be adapted to maintain the human-interpreta... | This paper develops a Shapley value approach to explanation that uses low-dimensional latent features to explain the original input. In high-dimensional settings, Shapley values can be computationally intractable; as such, the authors adapt the characteristic (aka value) function $v(\cdot)$ to consider coalitions defin... | SP:44a4a56c6048b2e7deb16dc106f4552e8f2227b9 |
Towards Robustness Against Natural Language Word Substitutions | 1 INTRODUCTION . Recent extensive studies have shown that deep neural networks ( DNNs ) are vulnerable to adversarial attacks ( Szegedy et al. , 2013 ; Goodfellow et al. , 2015 ; Papernot et al. , 2016a ; Kurakin et al. , 2017 ; Alzantot et al. , 2018 ) ; e.g. , minor phrase modification can easily deceive Google ’ s t... | of the paper*: This paper studies the problem of robustness against word substitutions. The authors propose a novel Adversarial Sparse Convex Combination (ASCC) method in which they model the word substitution attack space as a convex hull and leverages a regularization term to enforce perturbation towards an actual su... | SP:a013c72647ecd00dc627a8e501a8c171a870e3e6 |
Towards Robustness Against Natural Language Word Substitutions | 1 INTRODUCTION . Recent extensive studies have shown that deep neural networks ( DNNs ) are vulnerable to adversarial attacks ( Szegedy et al. , 2013 ; Goodfellow et al. , 2015 ; Papernot et al. , 2016a ; Kurakin et al. , 2017 ; Alzantot et al. , 2018 ) ; e.g. , minor phrase modification can easily deceive Google ’ s t... | In this paper, the authors aim to build a robust model against word substitution attacks. Unlike previous work, they consider a convex hull as the perturbation region instead of a norm-ball or a hyper-rectangle. From their derivation, perturbed words can be viewed as the linear combinations of substitutions and perturb... | SP:a013c72647ecd00dc627a8e501a8c171a870e3e6 |
Attention-Based Clustering: Learning a Kernel from Context | 1 INTRODUCTION . Many problems in machine learning involve modelling the relations between elements of a set . A notable example , and the focus of this paper , is clustering , in which the elements are grouped according to some shared properties . A common approach uses kernel methods : a class of algorithms that oper... | This paper proposes a method for producing representations for clustering that take into account global trends in the dataset, rather than considering each pair of instances in isolation. They claim to achieve competitive clustering performance on omniglot, which they attribute to the use of these contextualised embedd... | SP:e5bd789648c97f1c8836ec1e574f644e04a1c7da |
Attention-Based Clustering: Learning a Kernel from Context | 1 INTRODUCTION . Many problems in machine learning involve modelling the relations between elements of a set . A notable example , and the focus of this paper , is clustering , in which the elements are grouped according to some shared properties . A common approach uses kernel methods : a class of algorithms that oper... | The hypothesis of this paper is that learning a contextual metric (allowing pairwise distances to depend on the data) can improves clustering, and is motivated by two examples (Omniglot, intersecting circles). The paper proposes a new method - Attention based clustering (ABC) that incorporates context to learn a metri... | SP:e5bd789648c97f1c8836ec1e574f644e04a1c7da |
Predicting Inductive Biases of Pre-Trained Models | 1 INTRODUCTION . Large pre-trained language models ( LMs ) ( Devlin et al. , 2019 ; Raffel et al. , 2020 ; Brown et al. , 2020 ) have demonstrated impressive empirical success on a range of benchmark NLP tasks . However , analyses have shown that such models are easily fooled when tested on distributions that differ fr... | The paper aims to bridge the gap between model interpretation using probing and model's use of spurious features. They show that the findings of MDL with respect to a feature correlate with the extractability of the feature, given the evidence of representing the feature is available in the training data. The results a... | SP:ea63d76d9c20125dba72138cd644536bea909177 |
Predicting Inductive Biases of Pre-Trained Models | 1 INTRODUCTION . Large pre-trained language models ( LMs ) ( Devlin et al. , 2019 ; Raffel et al. , 2020 ; Brown et al. , 2020 ) have demonstrated impressive empirical success on a range of benchmark NLP tasks . However , analyses have shown that such models are easily fooled when tested on distributions that differ fr... | This paper studies the relationship between extractability of features from pre-trained representations and how much a fine-tuned model uses that feature. The extractability of features is measured by the minimum description length of a probing classifier trained to detect the feature from the pre-trained representatio... | SP:ea63d76d9c20125dba72138cd644536bea909177 |
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks | 1 INTRODUCTION . The need for protecting input privacy in Deep learning is growing rapidly in many areas such as health care ( Esteva et al. , 2019 ) , autonomous vehicles ( Zhu et al. , 2014 ) , finance ( Heaton et al. , 2017 ) , communication technologies ( Foerster et al. , 2016 ) etc . Many of the data holders are ... | This paper aims at addressing both secure training and inference of DNNs. The proposed method relies on securely off-loading the compute-intensive part of the operations from a trusted CPU environment which has low-performance, to an untrusted high-performance gpu. Their suggested method builds on SOTA, Slalom, but is... | SP:1feae47cc68e37f008c8358ed960afa41f9c0019 |
DarKnight: A Data Privacy Scheme for Training and Inference of Deep Neural Networks | 1 INTRODUCTION . The need for protecting input privacy in Deep learning is growing rapidly in many areas such as health care ( Esteva et al. , 2019 ) , autonomous vehicles ( Zhu et al. , 2014 ) , finance ( Heaton et al. , 2017 ) , communication technologies ( Foerster et al. , 2016 ) etc . Many of the data holders are ... | The paper builds on previous work like Slalom to propose a new secure training and inference protocol in the TEE+GPU paradigm. The main technical contribution of this work is a new blinding algorithm that dramatically reduces the memory required to store the blinding parameters (decoupling it from the input/model size)... | SP:1feae47cc68e37f008c8358ed960afa41f9c0019 |
Robust Imitation via Decision-Time Planning | The goal of imitation learning is to mimic expert behavior from demonstrations , without access to an explicit reward signal . A popular class of approach infers the ( unknown ) reward function via inverse reinforcement learning ( IRL ) followed by maximizing this reward function via reinforcement learning ( RL ) . The... | The paper considers imitation learning problem in the presence of small perturbations and nuisances at test-time. In particular, it proposes Imitation with Planning at Test-time (IMPLANT), a new algorithm for imitation learning that incorporates decision-time planning within an inverse reinforcement learning algorithm.... | SP:367b672094c7533843751a3f82a5cc073b1c3243 |
Robust Imitation via Decision-Time Planning | The goal of imitation learning is to mimic expert behavior from demonstrations , without access to an explicit reward signal . A popular class of approach infers the ( unknown ) reward function via inverse reinforcement learning ( IRL ) followed by maximizing this reward function via reinforcement learning ( RL ) . The... | The authors propose a method to enhance imitation learning by using MPC on the reward function learned by an imitation learning approach. The chosen MPC approach uses the learned policy to generate candidates for a search process that maximizes the reward. To learn the reward function, the authors propose to use GAIL. ... | SP:367b672094c7533843751a3f82a5cc073b1c3243 |
Manifold-aware Training: Increase Adversarial Robustness with Feature Clustering | 1 INTRODUCTION . 1.1 BACKGROUND . Convolutional neural networks ( CNNs ) are increasingly used in recent years due to their high adaptivity and flexibility . However , Szegedy et al . ( 2014 ) discovered that by maximizing the loss of a CNN model w.r.t the input data , one can find a small and imperceptible perturbatio... | Results: To defend against adversarial attacks, this work experimentally analyzes the feature distribution of traditionally- trained CNNs for gaining more knowledge about adversarial examples. Two properties, i.e., the non-clustering property and confusing-distance property, of the feature distribution are identified b... | SP:4fc7700df32695121eeb7c7a858077c45c50b44d |
Manifold-aware Training: Increase Adversarial Robustness with Feature Clustering | 1 INTRODUCTION . 1.1 BACKGROUND . Convolutional neural networks ( CNNs ) are increasingly used in recent years due to their high adaptivity and flexibility . However , Szegedy et al . ( 2014 ) discovered that by maximizing the loss of a CNN model w.r.t the input data , one can find a small and imperceptible perturbatio... | This paper tackles the problem of training models that are robust to adversarial inputs. The authors starts by observing that previous models generate embeddings that can both (i) place same-class embeddings in different clusters and (ii) different-class embeddings in close proximity. They then introduce new loss funct... | SP:4fc7700df32695121eeb7c7a858077c45c50b44d |
Visualizing High-Dimensional Trajectories on the Loss-Landscape of ANNs | 1 INTRODUCTION . Artificial neural networks ( ANNs ) have been successfully used to solve a number of complex tasks in a diverse array of domains , such as object recognition , machine translation , image generation , 3D protein structure prediction and many more . Despite being highly overparameterized for the tasks t... | This paper uses PHATE to visualize the progression of neural net parameters during learning in neural networks to provide insight into generalizable vs. non-generalizable minima, and the behaviors of different optimization algorithms. PHATE is an improvement over previous visualization techniques due to its approach t... | SP:1043d665e76e18b5418a66868d36575363717444 |
Visualizing High-Dimensional Trajectories on the Loss-Landscape of ANNs | 1 INTRODUCTION . Artificial neural networks ( ANNs ) have been successfully used to solve a number of complex tasks in a diverse array of domains , such as object recognition , machine translation , image generation , 3D protein structure prediction and many more . Despite being highly overparameterized for the tasks t... | The paper suggests using PHATE, a modern dimensionality reduction method, for visualizing the training trajectories of deep networks. It argues that PHATE visualizations can bring to light interesting aspects of the training dynamics that are missed by other dimensionality reduction algorithms because PHATE does a bett... | SP:1043d665e76e18b5418a66868d36575363717444 |
Rethinking Positional Encoding in Language Pre-training | 1 INTRODUCTION . The Transformer model ( Vaswani et al. , 2017 ) is the most widely used architecture in language representation learning ( Liu et al. , 2019 ; Devlin et al. , 2018 ; Radford et al. , 2019 ; Bao et al. , 2020 ) . In Transformer , positional encoding is an essential component since other main components ... | This paper studies the positional encoding in BERT. It argues against the word—position correlations that are implicitly imposed by BERT’s treatment of positional encodings. The paper proposes to decouple the “content attention” (token to token) and “contentless attention” (position to position), and remove the so call... | SP:ac65606f8823adff500d1e14e4f7fe5cac4e5b48 |
Rethinking Positional Encoding in Language Pre-training | 1 INTRODUCTION . The Transformer model ( Vaswani et al. , 2017 ) is the most widely used architecture in language representation learning ( Liu et al. , 2019 ; Devlin et al. , 2018 ; Radford et al. , 2019 ; Bao et al. , 2020 ) . In Transformer , positional encoding is an essential component since other main components ... | The paper delves into the nature of positional encoding in Transformer and variants (especially BERT). The paper points out that, attention weight computation obtained from the addition of the word embedding and the position embedding in the first layer can be expanded into four terms, namely word-word, position-positi... | SP:ac65606f8823adff500d1e14e4f7fe5cac4e5b48 |
GraphLog: A Benchmark for Measuring Logical Generalization in Graph Neural Networks | Relational inductive biases have a key role in building learning agents that can generalize and reason in a compositional manner . While relational learning algorithms such as graph neural networks ( GNNs ) show promise , we do not understand their effectiveness to adapt to new tasks . In this work , we study the logic... | The authors propose a synthetic graph generator to evaluate graph neural networks. The generation process starts with defining rules, subset of rules are used to define a world, each world is then used to sample a graph. Test queries are generated by picking a pair of vertices u, v and generating a path connecting them... | SP:3cdfd6f5f8455a41ef1edf46fada784b77b40cd7 |
GraphLog: A Benchmark for Measuring Logical Generalization in Graph Neural Networks | Relational inductive biases have a key role in building learning agents that can generalize and reason in a compositional manner . While relational learning algorithms such as graph neural networks ( GNNs ) show promise , we do not understand their effectiveness to adapt to new tasks . In this work , we study the logic... | This work proposes a method for generating synthetic datasets for testing path-based (knowledge) graph completion. Until recently, there were not many good benchmarks for evaluating reasoning with learned rules or learned knowledge, but there has been a fair amount of work on developing benchmarks for this lately. The ... | SP:3cdfd6f5f8455a41ef1edf46fada784b77b40cd7 |
Policy Learning Using Weak Supervision | 1 Introduction . Recent breakthroughs in policy learning ( PL ) open up the possibility to apply reinforcement learning ( RL ) or behavioral cloning ( BC ) in real-world applications such as robotics [ 1 , 2 ] and self-driving [ 3 , 4 ] . Most existing works require agents to receive high-quality supervision signals , ... | This paper tackles the problem of policy learning under weak/noisy supervision. The authors present PeerPL, a unified framework that can train agents using behavior cloning under noisy/suboptimal demonstrations, or using reinforcement learning under noisy rewards. PeerPL uses the idea of “Correlated Agreement” by subtr... | SP:fef1b68e8fefbd9ab2610b3c995a74852ce53a6a |
Policy Learning Using Weak Supervision | 1 Introduction . Recent breakthroughs in policy learning ( PL ) open up the possibility to apply reinforcement learning ( RL ) or behavioral cloning ( BC ) in real-world applications such as robotics [ 1 , 2 ] and self-driving [ 3 , 4 ] . Most existing works require agents to receive high-quality supervision signals , ... | This paper formulates a framework for reinforcement learning and behavior cloning from weak supervisions (i.e., noisy rewards or imperfect expert demonstration). Specifically, it proposes PeerPL to perform efficient policy learning from the available weak supervisions, which covers PeerRL (for RL with noisy rewards), P... | SP:fef1b68e8fefbd9ab2610b3c995a74852ce53a6a |
Non-Markovian Predictive Coding For Planning In Latent Space | 1 INTRODUCTION . Learning to control from high dimensional observations has been made possible due to the advancements in reinforcement learning ( RL ) and deep learning . These advancements have enabled notable successes such as solving video games ( Mnih et al. , 2015 ; Lample & Chaplot , 2017 ) and continuous contro... | This paper proposes an information-theoretic framework for learning a world model that encodes task-relevant information of the world. It shows that the learned encoder and dynamics model can be used to train the policy and fitting the value function to agent to perform comparibly well to Dreamer on standard tasks and ... | SP:c076b203ffada1be4399351d17008ce6fa54f441 |
Non-Markovian Predictive Coding For Planning In Latent Space | 1 INTRODUCTION . Learning to control from high dimensional observations has been made possible due to the advancements in reinforcement learning ( RL ) and deep learning . These advancements have enabled notable successes such as solving video games ( Mnih et al. , 2015 ; Lample & Chaplot , 2017 ) and continuous contro... | The paper proposes a method for visual model-based reinforcement learning that relies on contrastive learning to learn the predictive model. Building on Hafner’20, the paper replaces the image reconstruction objective with a noise contrastive estimation (NCE) objective for the latent dynamics model, an NCE objective be... | SP:c076b203ffada1be4399351d17008ce6fa54f441 |
Empirical Studies on the Convergence of Feature Spaces in Deep Learning | 1 INTRODUCTION . Blessed by the capacities of feature learning , deep neural networks ( DNNs ) ( LeCun et al. , 2015 ) have been widely used to perform learning tasks , ranging from classification , to generation ( Goodfellow et al. , 2014 ; Radford et al. , 2015 ) , in various settings ( e.g. , supervised , unsupervis... | This paper has a closer look at the distributions of samples in the feature space by utilizing P-vector to analyze principal subspace. According to their empirical studies, the authors concluded that the feature spaces learned by different deep models with the same dataset would share common principal subspaces for the... | SP:53ed366f6004f4c6edc7fcc5516d7e016c21ba91 |
Empirical Studies on the Convergence of Feature Spaces in Deep Learning | 1 INTRODUCTION . Blessed by the capacities of feature learning , deep neural networks ( DNNs ) ( LeCun et al. , 2015 ) have been widely used to perform learning tasks , ranging from classification , to generation ( Goodfellow et al. , 2014 ; Radford et al. , 2015 ) , in various settings ( e.g. , supervised , unsupervis... | The authors identify an interesting empirical phenomenon: across a range of network architectures and training approaches (supervised, unsupervised, auto-encoders), the feature spaces identified by these networks are similar. The authors introduce a specific way to summarize the feature space of a network as a vector (... | SP:53ed366f6004f4c6edc7fcc5516d7e016c21ba91 |
Secure Network Release with Link Privacy | 1 INTRODUCTION . Nowadays , open data of networks play a pivotal role in data mining and data analytics ( Tang et al. , 2008 ; Sen et al. , 2008 ; Blum et al. , 2013 ; Leskovec & Krevl , 2014 ) . By releasing and sharing structured relational data with research facilities and enterprise partners , data companies harves... | This work consider the problem of link privacy when releasing models that are trained on graph data. It achieves this by making a generative graph model based on a VAE differentially private. Differential privacy is obtained by adding noise to gradients during training (DPSGD approach). The paper uses graph metrics and... | SP:e0cd21da9c8cdb3bc34ae8ab2e7d2974a3d3e921 |
Secure Network Release with Link Privacy | 1 INTRODUCTION . Nowadays , open data of networks play a pivotal role in data mining and data analytics ( Tang et al. , 2008 ; Sen et al. , 2008 ; Blum et al. , 2013 ; Leskovec & Krevl , 2014 ) . By releasing and sharing structured relational data with research facilities and enterprise partners , data companies harves... | This paper considers the problem of releasing sensible structured data, where there are two inlined challenges: 1. The global network structure should be effectively preserved; 2. The link privacy should be rigorously protected. This paper looks at the secure release of network data with deep generative models. Specifi... | SP:e0cd21da9c8cdb3bc34ae8ab2e7d2974a3d3e921 |
TAM: Temporal Adaptive Module for Video Recognition | 1 INTRODUCTION . Deep learning has brought great progress for various recognition tasks in image domain , such as image classification ( Krizhevsky et al. , 2012 ; He et al. , 2016 ) , object detection ( Ren et al. , 2017 ) , and instance segmentation ( He et al. , 2017 ) . The key to these successes is to devise flexi... | This paper presents a new temporal adaptive module (TAM) to generate video-specific temporal kernels based on its own feature maps. TAM proposes a unique two-level adaptive modeling scheme by decoupling dynamic kernel into a location sensitive importance map and a location invariant aggregation weight. The importance m... | SP:aecbe1b77f8ad3df9f7377237cc47230b80ff50b |
TAM: Temporal Adaptive Module for Video Recognition | 1 INTRODUCTION . Deep learning has brought great progress for various recognition tasks in image domain , such as image classification ( Krizhevsky et al. , 2012 ; He et al. , 2016 ) , object detection ( Ren et al. , 2017 ) , and instance segmentation ( He et al. , 2017 ) . The key to these successes is to devise flexi... | This paper proposes a temporal adaptive module for video recognition. Specifically, it decouples dynamic kernel into a location sensitive importance map and a location invariant aggregation weight, which can be plugged into existing 2D CNNs to yield a powerful video architecture with small extra computational cost. The... | SP:aecbe1b77f8ad3df9f7377237cc47230b80ff50b |
Cubic Spline Smoothing Compensation for Irregularly Sampled Sequences | The marriage of recurrent neural networks and neural ordinary differential networks ( ODE-RNN ) is effective in modeling irregularly sampled sequences . While ODE produces the smooth hidden states between observation intervals , the RNN will trigger a hidden state jump when a new observation arrives and thus cause the ... | The paper builds on ODE-RNN model that allows to represent a time series as a continuous trajectory. The authors address the limitation of the ODE-RNN model that the trajectory is continuous everywhere except the observation points. They introduce a compensation term based on cubic splines that transforms the output tr... | SP:2a6bbbe26cf19664ffb879a8ea4fb9e3be195d1e |
Cubic Spline Smoothing Compensation for Irregularly Sampled Sequences | The marriage of recurrent neural networks and neural ordinary differential networks ( ODE-RNN ) is effective in modeling irregularly sampled sequences . While ODE produces the smooth hidden states between observation intervals , the RNN will trigger a hidden state jump when a new observation arrives and thus cause the ... | This work addresses the discontinuity issues caused by jumps in hidden state/output at the arrival of new observations in a ODE-RNN. This problem is tackled by adding a cubic spline smoothing component on top of ODE-RNN to produce smooth and continuous hidden state/outputs. They derive a closed form solution for the cu... | SP:2a6bbbe26cf19664ffb879a8ea4fb9e3be195d1e |
Grey-box Extraction of Natural Language Models | 1 INTRODUCTION . Machine learning models are often deployed behind APIs that enable querying the model but that prevent direct access to the model parameters . This restriction aims to protect intellectual property , as models are expensive to train and hence valuable ( Strubell et al. , 2019 ) ; security , as access t... | This paper proposes a range of algebraic model extraction attacks (different from the prevalent learning-based approaches) for transformer models trained for NLP tasks in a grey-box setting i.e., an existing, public, usually pretrained encoder, with a private classification layer. Through attacks on different sizes of ... | SP:7f4c42d8f72214b7a2ffc295358fd0e8f8a4103e |
Grey-box Extraction of Natural Language Models | 1 INTRODUCTION . Machine learning models are often deployed behind APIs that enable querying the model but that prevent direct access to the model parameters . This restriction aims to protect intellectual property , as models are expensive to train and hence valuable ( Strubell et al. , 2019 ) ; security , as access t... | This paper is an interesting study of algebraic model extraction attacks on modern NLP models based on BERT. Model extraction is the setting where a malicious attacker tries to reconstruct a copy of a black-box inference API without access to the original training data. Prior work [1] showed these attacks are possible ... | SP:7f4c42d8f72214b7a2ffc295358fd0e8f8a4103e |
Deepening Hidden Representations from Pre-trained Language Models | 1 INTRODUCTION . Language representation is essential to the understanding of text . Recently , pre-trained language models based on Transformer ( Vaswani et al. , 2017 ) such as GPT ( Radford et al. , 2018 ) , BERT ( Devlin et al. , 2019 ) , XLNet ( Yang et al. , 2019 ) , and RoBERTa ( Liu et al. , 2019c ) have been s... | This paper presents a new mechanism, called HIRE, to extract more information from the intermediate layers of pre-trained models, which will be further fused with the last layer of pre-trained models. The main contribution of this work is the newly proposed dynamic feature extractor HIRE and the fusion network. Experim... | SP:1bd140af937b949ece558c8978306e3e04d02169 |
Deepening Hidden Representations from Pre-trained Language Models | 1 INTRODUCTION . Language representation is essential to the understanding of text . Recently , pre-trained language models based on Transformer ( Vaswani et al. , 2017 ) such as GPT ( Radford et al. , 2018 ) , BERT ( Devlin et al. , 2019 ) , XLNet ( Yang et al. , 2019 ) , and RoBERTa ( Liu et al. , 2019c ) have been s... | The paper proposes a method to improve the downstream performance of a pretrained Transformer on NLP tasks. The core idea is to not only use the output of the last Transformer layer for prediction, but let the model decide how to fuse the information from intermediate layers as well. To dynamically decide which interme... | SP:1bd140af937b949ece558c8978306e3e04d02169 |
Efficient Generalized Spherical CNNs | Many problems across computer vision and the natural sciences require the analysis of spherical data , for which representations may be learned efficiently by encoding equivariance to rotational symmetries . We present a generalized spherical CNN framework that encompasses various existing approaches and allows them to... | The paper introduces a framework for computationally efficient and exactly rotation-equivariant spherical CNNs. The work most closely resembles the Fourier space method of Kondor et al., but improves on it in a number of ways: firstly, a channel-wise structure is introduced for the tensor product nonlinearities, which ... | SP:c175800a7bed98ed75736f032d6a5a9a2f3832c0 |
Efficient Generalized Spherical CNNs | Many problems across computer vision and the natural sciences require the analysis of spherical data , for which representations may be learned efficiently by encoding equivariance to rotational symmetries . We present a generalized spherical CNN framework that encompasses various existing approaches and allows them to... | This paper introduces a generalized spherical convolution operation that is strictly equivariant to rotation. The authors show that the spherical convolution operations introduced in prior works can be encompassed by the proposed approach. Because spherical convolutions introduce significant computational overhead, the... | SP:c175800a7bed98ed75736f032d6a5a9a2f3832c0 |
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds | 1 INTRODUCTION AND BACKGROUND . The endeavor to understand certain geometric aspects of decision problems has lead to intense research in statistical learning . These range from the study of data manifolds , through landscapes of loss functions to the delicate analysis of a classifier ’ s decision boundary . In the pre... | The paper proposes an isocapacitory measure for analysing decision bound, in a way complementing the isoperimetric analysis proposed by Ford et al. 2019. The authors showed that the new measure captures different geometric properties of the decision boundary, potentially useful for adversarial training. The paper also ... | SP:b76fa33429ee938e18037ddb4df37d2da952d566 |
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds | 1 INTRODUCTION AND BACKGROUND . The endeavor to understand certain geometric aspects of decision problems has lead to intense research in statistical learning . These range from the study of data manifolds , through landscapes of loss functions to the delicate analysis of a classifier ’ s decision boundary . In the pre... | The paper under review introduces a number of geometric measures (isoperimetric, isocapacitory ratios that relate to Brownian motion or heat diffusion probabilities) that are applied to study neural network decision boundaries locally. Specifically, the studies applying the measures to study adversarially trained NN e... | SP:b76fa33429ee938e18037ddb4df37d2da952d566 |
A Gradient Flow Framework For Analyzing Network Pruning | 1 INTRODUCTION . The use of Deep Neural Networks ( DNNs ) in intelligent edge systems has been enabled by extensive research on model compression . “ Pruning ” techniques are commonly used to remove “ unimportant ” filters to either preserve or promote specific , desirable model properties . Most pruning methods were o... | This paper proposes a detailed analysis on pruning heuristics, and its applications to early pruning. It thoroughly analyzed magnitude-based pruning, loss-preservation based pruning, and gradient-norm based pruning. The paper demonstrated the results on CIFAR-10 and CIFAR-100 datasets. it's very timely research to guid... | SP:9f7cb04c86bbb406040abc73f4f940af0c667d64 |
A Gradient Flow Framework For Analyzing Network Pruning | 1 INTRODUCTION . The use of Deep Neural Networks ( DNNs ) in intelligent edge systems has been enabled by extensive research on model compression . “ Pruning ” techniques are commonly used to remove “ unimportant ” filters to either preserve or promote specific , desirable model properties . Most pruning methods were o... | The paper contributes to explaining why saliency measures used for pruning trained models may (or may not) also be effective for pruning untrained or minimally trained models, by developing the relationship between those saliency measures and different forms of the norm of model parameters based on the evolution of mod... | SP:9f7cb04c86bbb406040abc73f4f940af0c667d64 |
Multi-Agent Trust Region Learning | 1 INTRODUCTION . Multi-agent systems ( MAS ) ( Shoham & Leyton-Brown , 2008 ) have received much attention from the reinforcement learning community . In real-world , automated driving ( Cao et al. , 2012 ) , StarCraft II ( Vinyals et al. , 2019 ) and Dota 2 ( Berner et al. , 2019 ) are a few examples of the myriad of ... | This paper proposes a modification of the Independent Learners trust region policy optimization method in general sum games. The modification consists of first forming a “meta game”—ie. the matrix game in which each agent’s options are his previous policy and his independent trust region optimized policy—-and then inte... | SP:10eba7b266e4d1443975f23593beae81e49a5051 |
Multi-Agent Trust Region Learning | 1 INTRODUCTION . Multi-agent systems ( MAS ) ( Shoham & Leyton-Brown , 2008 ) have received much attention from the reinforcement learning community . In real-world , automated driving ( Cao et al. , 2012 ) , StarCraft II ( Vinyals et al. , 2019 ) and Dota 2 ( Berner et al. , 2019 ) are a few examples of the myriad of ... | This paper presents a new trust-region method for multi-agent reinforcement learning (MARL). This approach extends ideas from single-agent trust-region methods to construct a smaller meta-game representing possible policy changes for each agent. The meta-game can then be solve to provide policy updates for the agents. ... | SP:10eba7b266e4d1443975f23593beae81e49a5051 |
Aspect-based Sentiment Classification via Reinforcement Learning | 1 INTRODUCTION . The goal of aspect-based ( also known as aspect-level ) sentiment classification is to predict the sentiment polarities of individual aspects . As shown in Figure 1 , given a sentence “ I like this computer but do not like the screen ” , the sentiment of the aspect “ computer ” is positive because of “... | The paper addresses aspect-based sentiment analysis by running reinforcement learning on the dependency parse of input sentences. The agent learns a policy network to select the most effective walk along edges in the dependency graphs, starting from the target aspect in the input sentences. The state representation is ... | SP:6cb958ab5e4337bc6d1d72324503a8362acead04 |
Aspect-based Sentiment Classification via Reinforcement Learning | 1 INTRODUCTION . The goal of aspect-based ( also known as aspect-level ) sentiment classification is to predict the sentiment polarities of individual aspects . As shown in Figure 1 , given a sentence “ I like this computer but do not like the screen ” , the sentiment of the aspect “ computer ” is positive because of “... | The paper proposes an approach to aspect-based sentiment classification, which is the task of identifying the sentiment of a specific phrase or entity in a sentence. The paper proposes to do this by first generating a dependency parse of the entire sentence and then using an RL agent to walk the dependency tree startin... | SP:6cb958ab5e4337bc6d1d72324503a8362acead04 |
GANs Can Play Lottery Tickets Too | 1 INTRODUCTION . Generative adversarial networks ( GANs ) have been successfully applied to many fields like image translation ( Jing et al. , 2019 ; Isola et al. , 2017 ; Liu & Tuzel , 2016 ; Shrivastava et al. , 2017 ; Zhu et al. , 2017 ) and image generation ( Miyato et al. , 2018 ; Radford et al. , 2016 ; Gulrajani... | The authors study the lottery ticket hypothesis for generative adversarial networks. Specifically, they attempt to answer the following questions: the existence of winning tickets in GANs; the effect of discriminator pruning in finding such winning tickets; the effect of initialization during the rewinding steps; and ... | SP:88ffd6498b2250b6b86a0e711446384df9285669 |
GANs Can Play Lottery Tickets Too | 1 INTRODUCTION . Generative adversarial networks ( GANs ) have been successfully applied to many fields like image translation ( Jing et al. , 2019 ; Isola et al. , 2017 ; Liu & Tuzel , 2016 ; Shrivastava et al. , 2017 ; Zhu et al. , 2017 ) and image generation ( Miyato et al. , 2018 ; Radford et al. , 2016 ; Gulrajani... | In this paper, the authors provide an empirical study on lottery ticket hypothesis on GANs. To do this, the authors use two GAN models and two datasets: SNGAN/CycleGAN and CIFAR-10/horse2zebra. Extensive experiments show that matching subnetworks can be found using unstructured magnitude pruning and channel pruning and... | SP:88ffd6498b2250b6b86a0e711446384df9285669 |
Debiasing Concept-based Explanations with Causal Analysis | 1 INTRODUCTION . Explaining the predictions of neural networks through higher level concepts ( Kim et al. , 2018 ; Ghorbani et al. , 2019 ; Brocki & Chung , 2019 ; Hamidi-Haines et al. , 2018 ) enables model interpretation on data with complex manifold structure such as images . It also allows the use of domain knowled... | Abstract: The paper proposed a way to learn unbiased (debiased) concept-based explainable models in the presence of unobserved confounders by the use of labels as instrumental variables. The proposed algorithm has 3 main steps: (1) regresses concept labels from the final labels (2) replace the original concepts with th... | SP:18f1b4ab7592fdcc814514e832b766dbb196d2b4 |
Debiasing Concept-based Explanations with Causal Analysis | 1 INTRODUCTION . Explaining the predictions of neural networks through higher level concepts ( Kim et al. , 2018 ; Ghorbani et al. , 2019 ; Brocki & Chung , 2019 ; Hamidi-Haines et al. , 2018 ) enables model interpretation on data with complex manifold structure such as images . It also allows the use of domain knowled... | The focus of the work is on model interpretability using concept-based explanation. The authors consider the issue of concepts being correlated with confounding information in the features. They propose a causal graph for representing the system and use instrumental variable methods to remove the impact of unobserved c... | SP:18f1b4ab7592fdcc814514e832b766dbb196d2b4 |
GAN "Steerability" without optimization | 1 INTRODUCTION . Since their introduction by Goodfellow et al . ( 2014 ) , generative adversarial networks ( GANs ) have seen remarkable progress , with current models capable of generating samples of very high quality ( Brock et al. , 2018 ; Karras et al. , 2019a ; 2018 ; 2019b ) . In recent years , particular effort ... | The authors propose two new techniques that extract interpretable directions from latent spaces of pretrained GAN generators. Both techniques are very efficient and are shown to work with the state-of-the-art BigGAN models. Furthermore, the authors describe additional details of the method, like determining the transfo... | SP:a5b02af3879a1add513f148bc12934e814258b09 |
GAN "Steerability" without optimization | 1 INTRODUCTION . Since their introduction by Goodfellow et al . ( 2014 ) , generative adversarial networks ( GANs ) have seen remarkable progress , with current models capable of generating samples of very high quality ( Brock et al. , 2018 ; Karras et al. , 2019a ; 2018 ; 2019b ) . In recent years , particular effort ... | This paper studies transformations in GAN latent space that map to meaningful transformations in the generated data. The main contribution is to derive closed form methods for discovering latent transformations that correspond to 1) geometric changes and 2) changes that capture principle components of model variation. ... | SP:a5b02af3879a1add513f148bc12934e814258b09 |
Hard Attention Control By Mutual Information Maximization | 1 INTRODUCTION . Reinforcement learning ( RL ) algorithms have successfully employed neural networks over the past few years , surpassing human level performance in many tasks ( Mnih et al. , 2015 ; Silver et al. , 2017 ; Berner et al. , 2019 ; Schulman et al. , 2017 ) . But a key difference in the way tasks are perfor... | This paper proposes a new architecture and training method to learn tasks that require hard attention control. Specifically, the paper proposes to learn the “glimpse agent” (which controls the hard attention window) by task-agnostic loss that seeks to maximize information gain by the glimpse to the learned world model.... | SP:73d7bceeae0307819f03d69e3799969808e20137 |
Hard Attention Control By Mutual Information Maximization | 1 INTRODUCTION . Reinforcement learning ( RL ) algorithms have successfully employed neural networks over the past few years , surpassing human level performance in many tasks ( Mnih et al. , 2015 ; Silver et al. , 2017 ; Berner et al. , 2019 ; Schulman et al. , 2017 ) . But a key difference in the way tasks are perfor... | This work presents a method for learning a hard attention controller using an information maximization approach. As the authors point out, such a method could be very useful for reasoning in terms of high-dimensional observations, like vision. In brief, the method learning to choose the next attention position to be th... | SP:73d7bceeae0307819f03d69e3799969808e20137 |
Geometry matters: Exploring language examples at the decision boundary | 1 INTRODUCTION . Machine learning classifiers have achieved state-of-the-art success in tasks such as image classification and text classification . Despite their successes , several recent papers have pointed out flaws in the features learned by such classifiers . Geirhos et al . ( 2020 ) cast this phenomenon as short... | This paper proposes an analysis technique for studying the 'difficulty' of a pair of test dataset examples in NLP. The setup proposed by past work on Contrast sets and Counterfactual Examples (Gardner et al, 2020 and Kaushik et al 2020 respectively) is to manually construct two dataset examples (x,y) with different lab... | SP:a2ee061eda974bd5c2c854e1c65e157e8d95c6cb |
Geometry matters: Exploring language examples at the decision boundary | 1 INTRODUCTION . Machine learning classifiers have achieved state-of-the-art success in tasks such as image classification and text classification . Despite their successes , several recent papers have pointed out flaws in the features learned by such classifiers . Geirhos et al . ( 2020 ) cast this phenomenon as short... | The authors argue that we should evaluate the robustness of NLP models near their decision boundaries, and argue that contrast sets and counterfactual examples cannot fullfill this purpose. The authors propose to find examples near the decision boundary using the largest eigenvalue of the Fisher information matrix, arg... | SP:a2ee061eda974bd5c2c854e1c65e157e8d95c6cb |
TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous Control | 1 INTRODUCTION . With the use of high-capacity function approximators , such as neural networks , reinforcement learning ( RL ) becomes practical in a wide range of real-world applications , including game playing ( Mnih et al. , 2013 ; Silver et al. , 2016 ) and robotic control ( Levine et al. , 2016 ; Haarnoja et al.... | This paper proposes Trust Entropy Actor Critic (TEAC), a novel algorithm for reinforcement learning (RL) combining the idea of TRPO/PPO and max-entropy RL, together with the corresponding critic, actor and dual updates. The high level idea is that trust region methods ensure stability by constraining the KL divergence ... | SP:be593818e1d32a8b3cdb66adcbf6daca5c56cf0c |
TEAC: Intergrating Trust Region and Max Entropy Actor Critic for Continuous Control | 1 INTRODUCTION . With the use of high-capacity function approximators , such as neural networks , reinforcement learning ( RL ) becomes practical in a wide range of real-world applications , including game playing ( Mnih et al. , 2013 ; Silver et al. , 2016 ) and robotic control ( Levine et al. , 2016 ; Haarnoja et al.... | The paper addresses the problem of reinforcement learning in continuous spaces by formulating the problem as a constrained optimization problem. In this problem, the objective is maximizing the expected reward, and the constraints ensure that 1) the distance between the new and old policies is bounded, 2) the entropy i... | SP:be593818e1d32a8b3cdb66adcbf6daca5c56cf0c |
Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy | 1 INTRODUCTION . Protecting data privacy is a significant concern in many data-driven decision-making applications ( Zhu et al. , 2017 ) , such as social networking service , recommender system , location-based service . For example , the United States Census Bureau will firstly employ differential privacy to the 2020 ... | This paper addresses the problem of an unbalanced data set. In particular, the accuracy on the well-represented classes is higher than the accuracy in underrepresented classes in an unbalanced dataset. This paper shows that DPSGD makes the problem of an unbalanced dataset even worse and decreases accuracy on the underr... | SP:53e0d7909b00c88201dc1d7a8da7bd1efa4eb48e |
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