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EMTL: A Generative Domain Adaptation Approach | We propose an unsupervised domain adaptation approach based on generative models . We show that when the source probability density function can be learned , one-step Expectation–Maximization iteration plus an additional marginal density function constraint will produce a proper mediator probability density function to... | This paper proposes a novel method for Unsupervised Domain Adaptation (UDA) when the source domain's privacy should be preserved. The authors propose EMTL, which is a generative method using multivariate densities using RNADE (Uria et al., 2013) and a mediator joint density function bridging both source and target doma... | SP:feed1c549e9d8bc680bfb92dbd0979b3fb103904 |
CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks | 1 INTRODUCTION . Graphs , as flexible data representations that store rich relational information , have been commonly used in data science tasks . Machine learning methods on graphs ( Chami et al. , 2020 ) , especially Graph Neural Networks ( GNNs ) , have attracted increasing interest in the research community . They... | The paper presents a new model based on the Graphical Neural Network (GNN). The proposed model adopts probability distributions called copulas and is called the Copula Graphical Neural Network (CopulaGNN). Two parametrizations of the CopulaGNN are given, and the learning of the proposed model is discussed. Experiments ... | SP:4ebb53f9acc9e99dc57bb71b548aabde7dccbef7 |
Active Tuning | 1 INTRODUCTION . Recurrent neural networks ( RNNs ) are inherently only robust against noise to a limited extent and they often generate unsuitable predictions when confronted with corrupted or missing data ( cf. , e.g. , Otte et al. , 2015 ) . To tackle noise , an explicit noise-aware training procedure can be employe... | Paper proposes a way to adapt an autoregressive model (RNN in examples) to the incoming noisy signal to generate noise-free data output. The approach is interesting due to applying updates to the hidden state of the past observation. The proposed approached is named Active Tuning and evaluated on 3 toy tasks. The idea ... | SP:78d44eef96138ddcb2b86cd1de3d9c6a63e33e32 |
Accelerating DNN Training through Selective Localized Learning | Training Deep Neural Networks ( DNNs ) places immense compute requirements on the underlying hardware platforms , expending large amounts of time and energy . We propose LoCal+SGD , a new algorithmic approach to accelerate DNN training by selectively combining localized or Hebbian learning within a Stochastic Gradient ... | This paper try to leverage the benefit of Hebb learning to reduce CNN training time cost. In order to achieve this, a learning mode selection algorithm is proposed to progressively increase number of layers using Hebb learning. The writing of this paper is good and the idea is also interesting, however, the experimen... | SP:2de60266ac8f4832460bd1da6451a74f63fd8f28 |
Accelerating DNN Training through Selective Localized Learning | Training Deep Neural Networks ( DNNs ) places immense compute requirements on the underlying hardware platforms , expending large amounts of time and energy . We propose LoCal+SGD , a new algorithmic approach to accelerate DNN training by selectively combining localized or Hebbian learning within a Stochastic Gradient ... | This paper proposes a combination of SGD with selective application of a non-backprop learning rule (Hebbian). The two learning rules are not applied together, but rather a boundary is determined where layers prior use SGD, and the ones after use the Hebbian approach. A selection algorithm dynamically adjusts the bound... | SP:2de60266ac8f4832460bd1da6451a74f63fd8f28 |
Action and Perception as Divergence Minimization | 1 INTRODUCTION . To achieve goals in complex environments , intelligent agents need to perceive their environments and choose effective actions . These two processes , perception and action , are often studied in isolation . Despite the many objectives that have been proposed in the fields of representation learning an... | The authors proposed to use the joint KL divergence between the generative joint distribution and the target distribution (containing latent variables which could correspond to latent parts we wanted to model (e.g. beliefs). It was illustrative to discuss decomposing the joint KL into different ways and thus forming in... | SP:3533f4976f70e2fdac0934dbb782d7b8af64c9fd |
Action and Perception as Divergence Minimization | 1 INTRODUCTION . To achieve goals in complex environments , intelligent agents need to perceive their environments and choose effective actions . These two processes , perception and action , are often studied in isolation . Despite the many objectives that have been proposed in the fields of representation learning an... | The authors formulate a general framework that unifies inference, action/perception, control, and several other tasks. The framework is based on minimizing the KL divergence between a parameterized "actual" distribution and a "target" distribution. The authors argue that this formulation unifies a wide range of previou... | SP:3533f4976f70e2fdac0934dbb782d7b8af64c9fd |
EigenGame: PCA as a Nash Equilibrium | 1 INTRODUCTION . The principal components of data are the vectors that align with the directions of maximum variance . These have two main purposes : a ) as interpretable features and b ) for data compression . Recent methods for principal component analysis ( PCA ) focus on the latter , explicitly stating objectives t... | Principal component analysis (PCA) is a well-known dimensionality reduction and feature learning technique in the literature that leads to uncorrelated features. While there are a plethora of algorithms for PCA, along with accompanying analysis, a majority of these works have been developed from an optimization perspec... | SP:9c77f92d9933964d7066aec0e5d3e33bb2ee1745 |
EigenGame: PCA as a Nash Equilibrium | 1 INTRODUCTION . The principal components of data are the vectors that align with the directions of maximum variance . These have two main purposes : a ) as interpretable features and b ) for data compression . Recent methods for principal component analysis ( PCA ) focus on the latter , explicitly stating objectives t... | The authors present new insights on PCA analysis by reconceiving it in terms of a Nash equilibrium among different players, related to the different components. The importance of an objective function minimizing the off-diagonal elements of R is emphasized. The insights lead to parallel algorithms and are demonstrated ... | SP:9c77f92d9933964d7066aec0e5d3e33bb2ee1745 |
Robust Temporal Ensembling | 1 INTRODUCTION . Deep neural networks have enjoyed considerable success across a variety of domains , and in particular computer vision , where the common theme is that more labeled training data yields improved model performance ( Hestness et al. , 2017 ; Mahajan et al. , 2018 ; Xie et al. , 2019b ; Kolesnikov et al. ... | Real-world data contains noise in the annotated labels. To mitigate, the authors propose a supervised learning approach, Robust Temporal Ensembling (RTE). RTE combines 1) task loss correction, which is a generalized cross entropy loss, 2) different augmentations resulting from AugMix technique and the Jensen-Shannon di... | SP:9feb34bfbe8bfbf1a99d90a74f36b2b0c7dc9985 |
Robust Temporal Ensembling | 1 INTRODUCTION . Deep neural networks have enjoyed considerable success across a variety of domains , and in particular computer vision , where the common theme is that more labeled training data yields improved model performance ( Hestness et al. , 2017 ; Mahajan et al. , 2018 ; Xie et al. , 2019b ; Kolesnikov et al. ... | This submission deals with robust supervised learning in the presence of noisy labels. The label noise is modeled using a probabilistic (and conditionally independent) transition matrix that changes the label of one class to another one. In order to classify with noise, the network is trained with a mixture of three kn... | SP:9feb34bfbe8bfbf1a99d90a74f36b2b0c7dc9985 |
Lipschitz-Bounded Equilibrium Networks | 1 INTRODUCTION . Deep neural network models have revolutionized the field of machine learning : their accuracy on practical tasks such as image classification and their scalability have led to an enormous volume of research on different model structures and their properties ( LeCun et al. , 2015 ) . In particular , dee... | The paper introduces a new condition for showing the existence of the solution of a deep equilibrium model (which defines an implicit mapping via the fixed point). The new formulation also comes with a convenient and accurate Lipschitz bound. The proposed condition can be satisfied via reparameterizing an unconstrained... | SP:16392bc9174dde6ad7b569f3f40fa14a4ed48831 |
Lipschitz-Bounded Equilibrium Networks | 1 INTRODUCTION . Deep neural network models have revolutionized the field of machine learning : their accuracy on practical tasks such as image classification and their scalability have led to an enormous volume of research on different model structures and their properties ( LeCun et al. , 2015 ) . In particular , dee... | > Summary: This paper studies a new and more general way of parameterizing the simplest equilibrium network of the form $\sigma(Wz+Ux+b)$, a form that has been tackled by works like (Winston & Kolter 2020)and (El Ghaoui et al. 2019). The authors provide a computationally (relatively) efficient way of computing Lipschit... | SP:16392bc9174dde6ad7b569f3f40fa14a4ed48831 |
Learning Safe Policies with Cost-sensitive Advantage Estimation | 1 INTRODUCTION . In recent years , Reinforcement Learning ( RL ) has achieved remarkable success in learning skillful AI agents in various applications ranging from robot locomotion ( Schulman et al. , 2015a ; Duan et al. , 2016 ; Schulman et al. , 2015c ) , video games ( Mnih et al. , 2015 ) and the game of Go ( Silve... | In this paper, the authors proposed a new constrained policy optimization algorithm and a worst-case version of the constrained MDP framework. Tho proposed constrained policy optimization algorithm is based on CPO, and a novel advantage function (CSAE) based on the concept of a "safe" state. Experiments in control simu... | SP:d7c00cd82b5d4cd035635e74b8525cf5603d305b |
Learning Safe Policies with Cost-sensitive Advantage Estimation | 1 INTRODUCTION . In recent years , Reinforcement Learning ( RL ) has achieved remarkable success in learning skillful AI agents in various applications ranging from robot locomotion ( Schulman et al. , 2015a ; Duan et al. , 2016 ; Schulman et al. , 2015c ) , video games ( Mnih et al. , 2015 ) and the game of Go ( Silve... | The authors propose to improve a safe RL algorithm, constrained policy optimizaiton, that can learn the optimal safe policy while exploring unsafe states less often during the training process. In particular, they dampen the estimated advantage associated with unsafe states, which encourages the RL algorithm to explore... | SP:d7c00cd82b5d4cd035635e74b8525cf5603d305b |
Combining Label Propagation and Simple Models out-performs Graph Neural Networks | 1 INTRODUCTION . Following the success of neural networks in computer vision and natural language processing , there are now a wide range of graph neural networks ( GNNs ) for making predictions involving relational data ( Battaglia et al. , 2018 ; Wu et al. , 2020 ) . These models have had much success and sit atop le... | This paper presents C&S method that predicts node labels in the transductive semi-supervised node classification setting. C&S uses the three-stage-pipeline approach. First, label probabilities are predicted with simple and scalable classifiers such as MLP. Then, the predicted errors are diffused over graphs. Finally, t... | SP:87fb323fc2a1b385c9a695c7669f509c835ef0aa |
Combining Label Propagation and Simple Models out-performs Graph Neural Networks | 1 INTRODUCTION . Following the success of neural networks in computer vision and natural language processing , there are now a wide range of graph neural networks ( GNNs ) for making predictions involving relational data ( Battaglia et al. , 2018 ; Wu et al. , 2020 ) . These models have had much success and sit atop le... | This paper shows modified label propagation can perform better than GCN. The idea is as follows: it first uses MLP on node features to get the initial labels, and then use two steps--correction and smoothness to postprocessing the labels. And this postprocessing is based on the traditional label propagation algorithm. ... | SP:87fb323fc2a1b385c9a695c7669f509c835ef0aa |
Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences | 1 INTRODUCTION . Graph neural networks ( GNNs ) have shown effectiveness in many fields with rich relational structures , such as citation networks ( Kipf & Welling , 2016 ; Veličković et al. , 2018 ) , social networks ( Hamilton et al. , 2017 ) , drug discovery ( Gilmer et al. , 2017 ; Stokes et al. , 2020 ) , physi... | The paper proposes a method called neighbor2seq that converts the hierarchical structure of the center node to a sequence during message passing in graph neural networks. The proposed method aims to mitigate the issue of excessive computation and memory requirement of training graph neural networks. The proposed models... | SP:6dbb656031537976500fc17775a52c782ef46729 |
Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences | 1 INTRODUCTION . Graph neural networks ( GNNs ) have shown effectiveness in many fields with rich relational structures , such as citation networks ( Kipf & Welling , 2016 ; Veličković et al. , 2018 ) , social networks ( Hamilton et al. , 2017 ) , drug discovery ( Gilmer et al. , 2017 ; Stokes et al. , 2020 ) , physi... | This paper proposed a simple graph neural network architecture that is easy to scale up and perform stochastic training. Instead of performing message passing as commonly used GNN, this paper first performs weighted combinations of node features per each hop of the neighbors of a center node, and then performs either C... | SP:6dbb656031537976500fc17775a52c782ef46729 |
Iterated learning for emergent systematicity in VQA | 1 INTRODUCTION . Although great progress has been made in visual question-answering ( VQA ) , recent methods still struggle to generalize systematically to inputs coming from a distribution different from that seen during training ( Bahdanau et al. , 2019b ; a ) . Neural module networks ( NMNs ) present a natural solut... | The authors address methods to encourage the emergence of the layout expression structures on the frameworks of neural module networks (NMN) for the visual QA problems. The methods are motivated from the works on language emergence for communication between multi-agents and the language acquisition of new-born babies f... | SP:c7f896d15bb66637e8ad0b80f7baa713d9da6c30 |
Iterated learning for emergent systematicity in VQA | 1 INTRODUCTION . Although great progress has been made in visual question-answering ( VQA ) , recent methods still struggle to generalize systematically to inputs coming from a distribution different from that seen during training ( Bahdanau et al. , 2019b ; a ) . Neural module networks ( NMNs ) present a natural solut... | The authors apply iterated learning - a procedure originating in CogSci analyses of how human languages might develop - to the training of neural module networks. The goal is for iterated learning to encourage these networks to develop compositional structures that support systematic generalization without requiring ex... | SP:c7f896d15bb66637e8ad0b80f7baa713d9da6c30 |
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains | 1 INTRODUCTION . Batch normalization ( Ioffe & Szegedy , 2015 ) is crucial for training neural networks , and with its variants , e.g. , layer normalization ( Ba et al. , 2016 ) , group normalization ( Wu & He , 2018 ) and instance normalization ( Ulyanov et al. , 2016 ) , has thus become an essential part of the deep ... | This paper describes a new method for normalizing few-shot learning episodes. The authors point out that the statistics of an episode are unreliable when the size of the episode is small or when the data distribution changes from episode to episode. To remedy this, the authors propose a method called ‘MetaNorm’ which u... | SP:ea7daa9dbbcba08e7c094630ef2bb55badc4fde5 |
MetaNorm: Learning to Normalize Few-Shot Batches Across Domains | 1 INTRODUCTION . Batch normalization ( Ioffe & Szegedy , 2015 ) is crucial for training neural networks , and with its variants , e.g. , layer normalization ( Ba et al. , 2016 ) , group normalization ( Wu & He , 2018 ) and instance normalization ( Ulyanov et al. , 2016 ) , has thus become an essential part of the deep ... | This paper proposes to replace batch normalization statistics, which are typically computed as the batch moments during training or a fixed training average during testing, with the outputs of learned neural networks. These networks are trained to minimize the KL divergence between their output and the expected or desi... | SP:ea7daa9dbbcba08e7c094630ef2bb55badc4fde5 |
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? | 1 INTRODUCTION . Deep neural networks have become one of the most important and prevalent machine learning models due to their remarkable power in many real-world applications . However , the success of deep learning has not been well-explained in theory . It remains mysterious why standard optimization algorithms tend... | The paper extends an existing proof for the sufficiency of polylogarithmic width for sharp learning guarantees of ReLU networks trained by (stochastic) gradient descent from shallow networks to deep networks. The theoretical analysis links the convergence of GD and SGD to the width of the network. The paper shows that ... | SP:a81ee1b76201649dc0d0653db304c7297befee33 |
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks? | 1 INTRODUCTION . Deep neural networks have become one of the most important and prevalent machine learning models due to their remarkable power in many real-world applications . However , the success of deep learning has not been well-explained in theory . It remains mysterious why standard optimization algorithms tend... | The paper studies optimization and generalization properties of deep relu networks trained with (stochastic) gradient descent on the logistic loss in the neural tangent kernel (NTK) regime. By using a new analysis that makes the "linearized" approximation as well as the L2 norm of the model in the approximate "random f... | SP:a81ee1b76201649dc0d0653db304c7297befee33 |
Balancing training time vs. performance with Bayesian Early Pruning | 1 INTRODUCTION . Deep neural networks ( DNNs ) are known to be overparameterized ( Allen-Zhu et al. , 2019 ) as they usually have more learnable parameters than needed for a given learning task . So , a trained DNN contains many ineffectual parameters that can be safely pruned or zeroed out with little/no effect on its... | This paper introduces a method for pruning during the training process in order to filter out unimportant/redundant components of the network continuously to speed up training and perform gradual pruning over the training process. The proposed approach is novel in the sense that the vast amount of prior work on pruning... | SP:e7caebe84a63ae1f2e8eda175eec514684a7a2ee |
Balancing training time vs. performance with Bayesian Early Pruning | 1 INTRODUCTION . Deep neural networks ( DNNs ) are known to be overparameterized ( Allen-Zhu et al. , 2019 ) as they usually have more learnable parameters than needed for a given learning task . So , a trained DNN contains many ineffectual parameters that can be safely pruned or zeroed out with little/no effect on its... | This paper introduces a new method to accelerate training by saliency-based pruning. The method predicts future saliency for neurons based on observed saliency with a multi-output Gaussian process (MOGP), then greedily prunes neurons with least saliency at fixed intervals during training. The authors provide extensive ... | SP:e7caebe84a63ae1f2e8eda175eec514684a7a2ee |
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity | 1 INTRODUCTION . Q-learning ( Watkins , 1989 ) and its deep learning based successors inaugurated by DQN ( Mnih et al. , 2015 ) are model-free , value function based reinforcement learning algorithms . Their popularity stems from their intuitive , easy-to-implement update rule derived from the Bellman equation . At eac... | This paper proposes methods to induce diversity in the networks of ensemble-based Q-Learning methods. This is achieved my maximizing a variety of measures of inequality based on the L2 parameter norms of individual networks in an ensemble. This is motivated by the benefit of having diversity in the learned features, wh... | SP:eb16e608d4bb9be2c7f2e358a5166c6c202272cc |
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity | 1 INTRODUCTION . Q-learning ( Watkins , 1989 ) and its deep learning based successors inaugurated by DQN ( Mnih et al. , 2015 ) are model-free , value function based reinforcement learning algorithms . Their popularity stems from their intuitive , easy-to-implement update rule derived from the Bellman equation . At eac... | Q-learning is known to have overestimation bias. Approaches like EnsembleDQN and MaxminDQN try to use different estimates from ensembles of learners to reduce the bias. The authors study a specific observation and try to tackle it by regularization technique to maximise the diversity of representation space. Five diffe... | SP:eb16e608d4bb9be2c7f2e358a5166c6c202272cc |
Brain-like approaches to unsupervised learning of hidden representations - a comparative study | 1 INTRODUCTION . Artificial neural networks have made remarkable progress in supervised pattern recognition in recent years . In particular , deep neural networks have dominated the field largely due to their capability to discover hierarchies of salient data representations . However , most recent deep learning method... | This paper evaluated four unsupervised learning approaches (BCPNN, KH, RBM, AE) by training a supervised classification layer on top of the hidden representation. Specifically, the authors qualitatively compared the receptive fields and quantitatively compared the classification performance across four models. The auth... | SP:f746ca9d21491dd433de8667cb51e6a137f2898f |
Brain-like approaches to unsupervised learning of hidden representations - a comparative study | 1 INTRODUCTION . Artificial neural networks have made remarkable progress in supervised pattern recognition in recent years . In particular , deep neural networks have dominated the field largely due to their capability to discover hierarchies of salient data representations . However , most recent deep learning method... | The Bayesian Confidence Propagating Neural Network has recently been extended to the case of unsupervised learning (Ravichandran et al., IJCNN, 2020). This paper compares this extension to restricted Boltzmann machines, autoencoders, and a biologically plausible model proposed by (Krotov & Hopfield, PNAS, 2019) on the ... | SP:f746ca9d21491dd433de8667cb51e6a137f2898f |
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay | 1 INTRODUCTION . Success stories of deep reinforcement learning ( RL ) from high dimensional inputs such as pixels or large spatial layouts include achieving superhuman performance on Atari games ( Mnih et al. , 2015 ; Schrittwieser et al. , 2019 ; Badia et al. , 2020 ) , grandmaster level in Starcraft II ( Vinyals et ... | This work proposes LeVER, a method that modifies general off-policy RL algorithms with a fixed layer freezing policy for early embedding layers (in this particular case, a few early layers of a CNN). As a direct consequence, the method enables to store embeddings in the experience replay buffer rather than observations... | SP:66df426d54b2965855f955ec2946f5304b974ef5 |
Compute- and Memory-Efficient Reinforcement Learning with Latent Experience Replay | 1 INTRODUCTION . Success stories of deep reinforcement learning ( RL ) from high dimensional inputs such as pixels or large spatial layouts include achieving superhuman performance on Atari games ( Mnih et al. , 2015 ; Schrittwieser et al. , 2019 ; Badia et al. , 2020 ) , grandmaster level in Starcraft II ( Vinyals et ... | This manuscript proposes to reduce the intensive computation and memory requirement in reinforcement learning trainings by freezing the parameters of lower layers early. Besides, the authors also propose to store the low-dimensional latent vectors rather than the high-dimensional images in the replay buffer for experie... | SP:66df426d54b2965855f955ec2946f5304b974ef5 |
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization | 1 INTRODUCTION . Model-based Reinforcement Learning ( RL ) aims to learn an approximate model of the environment ’ s dynamics from existing logged interactions to facilitate efficient policy evaluation and optimization . Early work on Model-based RL uses simple tabular ( Sutton , 1990 ; Moore and Atkeson , 1993 ; Peng ... | The authors consider the usage of autoregressive dynamics models for batch model-based RL, where state-variable/reward predictions are performed sequentially conditioned on previously-predicted variables. Extensive numerical results are provided in several continuous domains for both policy evaluation and optimization ... | SP:686d12e3c1b9b03b8a0ad2106de8108b793daab3 |
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization | 1 INTRODUCTION . Model-based Reinforcement Learning ( RL ) aims to learn an approximate model of the environment ’ s dynamics from existing logged interactions to facilitate efficient policy evaluation and optimization . Early work on Model-based RL uses simple tabular ( Sutton , 1990 ; Moore and Atkeson , 1993 ; Peng ... | The paper studies offline policy evaluation (OPE) and optimization in the model-based setting. The main methodological contribution of the paper is using autoregressive models for the next state and reward prediction. The authors demonstrate that autoregressive models achieve higher likelihood compared to feedforward m... | SP:686d12e3c1b9b03b8a0ad2106de8108b793daab3 |
Differential-Critic GAN: Generating What You Want by a Cue of Preferences | 1 INTRODUCTION . Learning a good generative model for high-dimensional natural signals , such as images ( Zhu et al. , 2017 ) , video ( Vondrick et al. , 2016 ) and audio ( Fedus et al. , 2018 ) has long been one of the key milestones of machine learning . Powered by the learning capabilities of deep neural networks , ... | The motivation of this study is to estimate the distribution of desired data from the entire data distribution. And the proposed solution extends existing GAN solutions by introducing an additional pairwise loss on the discriminator, e.g., its scores on the desired instances should be higher than the undesired ones. Th... | SP:64282a23a9df8092c2fc9737045a96d1ac64f4ac |
Differential-Critic GAN: Generating What You Want by a Cue of Preferences | 1 INTRODUCTION . Learning a good generative model for high-dimensional natural signals , such as images ( Zhu et al. , 2017 ) , video ( Vondrick et al. , 2016 ) and audio ( Fedus et al. , 2018 ) has long been one of the key milestones of machine learning . Powered by the learning capabilities of deep neural networks , ... | The authors introduce DiCGAN, an algorithm to learn a generative model that comes up with samples whose likelihood is based on a real dataset but adjusted given user preferences. They train the critic to assign high values to samples with higher preference values and thus the generator tends to move its samples towards... | SP:64282a23a9df8092c2fc9737045a96d1ac64f4ac |
Understanding and Improving Lexical Choice in Non-Autoregressive Translation | 1 INTRODUCTION . When translating a word , translation models need to spend a substantial amount of its capacity in disambiguating its sense in the source language and choose a lexeme in the target language which adequately express its meaning ( Choi et al. , 2017 ; Tamchyna , 2017 ) . However , neural machine translat... | This paper follows up on the work (Zhou et al.) on establishing the importance of knoweldge distillation (KD) from a pretrained autoregressive translation model (AT) to train effective non-autoregresstive translation (NAT) models. Specifically, KD is helpful because it reduces the data complexity which allows successf... | SP:18ce50996a98836e07d8cb448adbff5cb039b285 |
Understanding and Improving Lexical Choice in Non-Autoregressive Translation | 1 INTRODUCTION . When translating a word , translation models need to spend a substantial amount of its capacity in disambiguating its sense in the source language and choose a lexeme in the target language which adequately express its meaning ( Choi et al. , 2017 ; Tamchyna , 2017 ) . However , neural machine translat... | This paper analyzes the side effect of knowledge distillation in NAT where the lexical choice errors on low-frequency words are propagated to the student model from the teacher. Tackling on this, the paper then proposes to expose raw data to restore such information. In my view, the submission is well motivated and the... | SP:18ce50996a98836e07d8cb448adbff5cb039b285 |
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy | Regularization plays a crucial role in machine learning models , especially for deep neural networks . The existing regularization techniques mainly rely on the i.i.d . assumption and only consider the knowledge from the current sample , without the leverage of neighboring relationship between samples . In this work , ... | This paper proposed a new regularization method via patch level interpolation. During the training, images within a batch will be used to construct an image graph. For example, for a certain image, its nearest neighbors in the feature spaces will be used. Then patches from its neighbors will be used to interpolate t... | SP:21d29b68bb3e7cf18e699a98f7be35f9e12bdaaf |
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy | Regularization plays a crucial role in machine learning models , especially for deep neural networks . The existing regularization techniques mainly rely on the i.i.d . assumption and only consider the knowledge from the current sample , without the leverage of neighboring relationship between samples . In this work , ... | The paper proposes a general regularizer called the Patch-level Neighborhood Interpolation (Pani) that constructs patch-level graphs at different levels of neural networks. Specifically, it is based on the k-nearest patch neighbors at each layer and linear interpolation for each patch. By applying this proposed regular... | SP:21d29b68bb3e7cf18e699a98f7be35f9e12bdaaf |
Differentiable Trust Region Layers for Deep Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning has shown considerable advances in recent years with prominent application areas such as games ( Mnih et al. , 2015 ; Silver et al. , 2017 ) , robotics ( Levine et al. , 2015 ) , and control ( Duan et al. , 2016 ) . In policy search , policy gradient ( PG ) methods have been... | In trust-region-based policy optimization methods such as TRPO and PPO, it is difficult to tune and lots of approximations are required. The authors try to solve this issue by introducing the closed-form derivation of trust regions for Gaussian policies with three different types of divergence (or distance). Based on t... | SP:7a6904083c223c746197e75e6b24d84107b50ab3 |
Differentiable Trust Region Layers for Deep Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning has shown considerable advances in recent years with prominent application areas such as games ( Mnih et al. , 2015 ; Silver et al. , 2017 ) , robotics ( Levine et al. , 2015 ) , and control ( Duan et al. , 2016 ) . In policy search , policy gradient ( PG ) methods have been... | The paper proposes a way to impose trust region restrictions via projections when doing policy optimisation in Reinforcement Learning. The projections have a closed form and enforce a trust region for each state individually. The authors propose three types of projections based on Frobenius, Wasserstein distances and K... | SP:7a6904083c223c746197e75e6b24d84107b50ab3 |
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning | 1 INTRODUCTION . “ We use imagination in our ordinary perception of the world . This perception can not be separated from interpretation. ” ( Warnock , 1976 ) . The human brain , and the mind that emerges from its working , is currently our best example of a general purpose intelligent learning system . And our ability... | This paper proposes to combine model-based and multi-agent reinforcement learning. The authors follow the typical recurrent neural world models setting to generate imagined rollouts for decision-time planning. To tackle the non-stationarity of a multi-agent environment, they build end-to-end differentiable communicatio... | SP:e4eac7e23932f7b1c1ac0c281cbeb076a4525a86 |
Learning to communicate through imagination with model-based deep multi-agent reinforcement learning | 1 INTRODUCTION . “ We use imagination in our ordinary perception of the world . This perception can not be separated from interpretation. ” ( Warnock , 1976 ) . The human brain , and the mind that emerges from its working , is currently our best example of a general purpose intelligent learning system . And our ability... | The paper talks about developing a model-based method for cooperative multi-agent reinforcement learning. The proposed approach utilizes communication as a tool for mitigating the partial observability induced by the non-stationary task while also helping agents reason about other agents' behaviors. The authors present... | SP:e4eac7e23932f7b1c1ac0c281cbeb076a4525a86 |
Generating Plannable Lifted Action Models for Visually Generated Logical Predicates | 1 INTRODUCTION . Learning a high-level symbolic transition model of an environment from raw input ( e.g. , images ) is a major challenge in the integration of connectionism and symbolism . Doing so without manually defined symbols is particularly difficult as it requires solving both the Symbol Grounding ( Harnad , 199... | This work presents FOSAE++, an end-to-end system capable of producing "lifted" action models provided only bounding box annotations of image pairs before and after an unknown action is executed. Building on recent work in the space, the primary contribution of this work is to generate PDDL action rules. To accomplish t... | SP:78f30ff42b38782a096376e39364151da28d1812 |
Generating Plannable Lifted Action Models for Visually Generated Logical Predicates | 1 INTRODUCTION . Learning a high-level symbolic transition model of an environment from raw input ( e.g. , images ) is a major challenge in the integration of connectionism and symbolism . Doing so without manually defined symbols is particularly difficult as it requires solving both the Symbol Grounding ( Harnad , 199... | This paper addresses the problem of learning dynamics model directly from raw sensory inputs. The authors propose an unsupervised end-to-end model that can perform high-level tasks planning on raw observations. This work extends Asai et al. 2020, 2019 etc, and with improved symbol generation and lifted PDDL. The author... | SP:78f30ff42b38782a096376e39364151da28d1812 |
Linear Convergent Decentralized Optimization with Compression | 1 INTRODUCTION . Distributed optimization solves the following optimization problem x∗ : = argmin x∈Rd [ f ( x ) : = 1 n n∑ i=1 fi ( x ) ] ( 1 ) with n computing agents and a communication network . Each fi ( x ) : Rd → R is a local objective function of agent i and typically defined on the data Di settled at that agen... | The paper introduces a novel decentralized algorithm (LEAD) incorporated with compression that achieves linear convergence rate in strongly convex setting. The main idea is to apply and communicate the compression of an auxiliary variable instead of the primal or dual iterates. Convergence analysis is provided for bot... | SP:940f5374980f33ee94784370eccd403e49c99ac3 |
Linear Convergent Decentralized Optimization with Compression | 1 INTRODUCTION . Distributed optimization solves the following optimization problem x∗ : = argmin x∈Rd [ f ( x ) : = 1 n n∑ i=1 fi ( x ) ] ( 1 ) with n computing agents and a communication network . Each fi ( x ) : Rd → R is a local objective function of agent i and typically defined on the data Di settled at that agen... | This paper introduces a novel algorithm for decentralized optimization when nodes can only communicate a compressed signal with their neighbors. Unlike most decentralized methods with compression that are inspired by primal methods (DGD type methods), this paper introduces a new primal-dual algorithm with compression. ... | SP:940f5374980f33ee94784370eccd403e49c99ac3 |
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games | Training agents using Reinforcement Learning with sparse rewards is often difficult ( Pathak et al. , 2017 ) . First , due to the sparsity of the reward , the agent often spends the majority of the training time doing inefficient exploration and sometimes not even reaching the first sparse reward during the entirety of... | This paper introduces an approach called action guidance, made to address issues in more standard applications of reward shaping. The main idea of their approach is that there are two different kinds of agents, one (auxiliary agents) that learn from shaped reward functions alone and the other (main agent(s)) that learn... | SP:c0924c1c4d4132e6d80e24103c243780438f8a89 |
Action Guidance: Getting the Best of Sparse Rewards and Shaped Rewards for Real-time Strategy Games | Training agents using Reinforcement Learning with sparse rewards is often difficult ( Pathak et al. , 2017 ) . First , due to the sparsity of the reward , the agent often spends the majority of the training time doing inefficient exploration and sometimes not even reaching the first sparse reward during the entirety of... | The paper introduces an approach for learning policies across multiple MDPs and using those policies to improve learning performance on the task that the agent designer cares about. The approach assumes that a set of MDPs are provided to the learning agent, and that all of the MDPs have the same underlying task but wit... | SP:c0924c1c4d4132e6d80e24103c243780438f8a89 |
Learning Hyperbolic Representations for Unsupervised 3D Segmentation | There exists a need for unsupervised 3D segmentation on complex volumetric data , particularly when annotation ability is limited or discovery of new categories is desired . Using the observation that much of 3D volumetric data is innately hierarchical , we propose learning effective representations of 3D patches for u... | The authors of this manuscript propose an unsupervised learning framework for 3D segmentation of biomedical images. Specifically, the proposed method learns effective representations for 3D patches using variational autoencoder (VAE) with a hyperbolic latent space. Its main contribution lies at that it introduces a new... | SP:d197f9ea345b135b417400d791002f18baad39e7 |
Learning Hyperbolic Representations for Unsupervised 3D Segmentation | There exists a need for unsupervised 3D segmentation on complex volumetric data , particularly when annotation ability is limited or discovery of new categories is desired . Using the observation that much of 3D volumetric data is innately hierarchical , we propose learning effective representations of 3D patches for u... | The paper considers learning hyperbolic representations for unsupervised 3D segmentation. Since the general task of producing annotations for 3D data can be expensive (e.g. for segmentation in dense voxel grids), this is an important problem. The paper proposes to learn hierarchical data structures (e.g. 3D biomedical ... | SP:d197f9ea345b135b417400d791002f18baad39e7 |
One-class Classification Robust to Geometric Transformation | 1 INTRODUCTION . One-class classification refers to the problem of identifying whether an input example belongs to a single target class ( in-class ) or any of novel classes ( out-of-class ) . The main challenge of this task is that only in-class examples are available at training time . Thus , by using only positive e... | This paper considers the deep one-class classification problem. Some recent state of the art in this area is built upon self-supervised learning methods that are trained to predict the rotation applied to a training image, and then use the success of rotation prediction on test images as an outlier score. The paper obs... | SP:70bb2ad8b8a46670e6ee60a6800656c4f2220ad0 |
One-class Classification Robust to Geometric Transformation | 1 INTRODUCTION . One-class classification refers to the problem of identifying whether an input example belongs to a single target class ( in-class ) or any of novel classes ( out-of-class ) . The main challenge of this task is that only in-class examples are available at training time . Thus , by using only positive e... | This paper presents a one-class classifier robust to geometrically-transformed inputs (GROC). A conformity score is proposed that measures how strongly an input image agrees with one of the predefined in-class transformations. Experiments show that the proposed method works well on 3 datasets for out-of-class detection... | SP:70bb2ad8b8a46670e6ee60a6800656c4f2220ad0 |
Weights Having Stable Signs Are Important: Finding Primary Subnetworks and Kernels to Compress Binary Weight Networks | 1 INTRODUCTION . Convolutional Neural Networks ( CNNs ) have achieved great success in many computer vision tasks such as image classification ( Krizhevsky et al. , 2012 ) , object detection ( Girshick et al. , 2014 ) and semantic segmentation ( Long et al. , 2015 ) . However , modern CNNs usually have large number of ... | The Authors show that scaling factors with hand-crafted or learnable methods are not so important when training Binary Weight Networks (BWNs), while the change of weight signs is crucial. They make two observations: The weight signs of the primary binary sub-networks are determined and fixed at the early training stage... | SP:fdf6eccb626f29ace14ead921e976448e2dd8bb8 |
Weights Having Stable Signs Are Important: Finding Primary Subnetworks and Kernels to Compress Binary Weight Networks | 1 INTRODUCTION . Convolutional Neural Networks ( CNNs ) have achieved great success in many computer vision tasks such as image classification ( Krizhevsky et al. , 2012 ) , object detection ( Girshick et al. , 2014 ) and semantic segmentation ( Long et al. , 2015 ) . However , modern CNNs usually have large number of ... | This paper proposes some interesting observations for training BWNs. 1: The scaling factors can be removed with batch normalization used. 2: The signs of the weights with large norms are determined and fixed at the early training stage. 3: The binary weight networks can be further compressed. Moreover, the authors pro... | SP:fdf6eccb626f29ace14ead921e976448e2dd8bb8 |
Class Balancing GAN with a Classifier in the Loop | 1 INTRODUCTION . Image Generation witnessed unprecedented success in recent years following the invention of Generative Adversarial Networks ( GANs ) by Goodfellow et al . ( 2014 ) . GANs have improved significantly over time with the introduction of better architectures ( Gulrajani et al. , 2017 ; Radford et al. , 201... | **Overview**: The paper presents a simple regularizer term that aims to force a GAN to generate samples following a uniform distribution over different classes. The regularizer depends on a classifier that works well on an imbalanced or long-tailed dataset. The paper presents experiments on CIFAR-10 and LSUN that were ... | SP:c343c46cd2f33ae06be87cf9b44fbdbd59f335cd |
Class Balancing GAN with a Classifier in the Loop | 1 INTRODUCTION . Image Generation witnessed unprecedented success in recent years following the invention of Generative Adversarial Networks ( GANs ) by Goodfellow et al . ( 2014 ) . GANs have improved significantly over time with the introduction of better architectures ( Gulrajani et al. , 2017 ; Radford et al. , 201... | The paper proposes a regularizer to force an unconditional GAN generator to produce samples that follow a uniform class distribution. To provide feedback to the generator about the class distribution over the generated images, the proposed method utilizes a pretrained classifier on the same (imbalanced) training datase... | SP:c343c46cd2f33ae06be87cf9b44fbdbd59f335cd |
GraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement | 1 INTRODUCTION . Representing nodes or entire graphs with informative low-dimensional feature vectors plays a crucial role in many real-world applications and domains , e.g . user analysis in social networks ( Tan et al. , 2011 ; Yan et al. , 2013 ) , relational inference in knowledge graphs ( Bordes et al. , 2013 ; Tr... | This paper presents a novel method called Embed-SAD (as well as Input-SAD) to learn graph/node representations to disentangle structure and attribute information. Input-SAD is a simple baseline that tries to get structure-attribute disentanglements by individually processing graph structures and node attributes. For st... | SP:d6ecb075f238cc67a6cc4f6b924e1b7b3eb69dfa |
GraphSAD: Learning Graph Representations with Structure-Attribute Disentanglement | 1 INTRODUCTION . Representing nodes or entire graphs with informative low-dimensional feature vectors plays a crucial role in many real-world applications and domains , e.g . user analysis in social networks ( Tan et al. , 2011 ; Yan et al. , 2013 ) , relational inference in knowledge graphs ( Bordes et al. , 2013 ; Tr... | This paper focuses on disentangling embeddings of the structure and the attribute of graph. The authors' key idea is that the structure and attribute information should be split in GNN. Based on this, the authors try to disentangle the structure embedding and the attribute embedding. With two different components, two... | SP:d6ecb075f238cc67a6cc4f6b924e1b7b3eb69dfa |
NBDT: Neural-Backed Decision Tree | 1 INTRODUCTION . Many computer vision applications ( e.g . medical imaging and autonomous driving ) require insight into the model ’ s decision process , complicating applications of deep learning which are traditionally black box . Recent efforts in explainable computer vision attempt to address this need and can be g... | The paper proposes a method to make neural networks more accurate and interpretable by replacing their final layers with a probabilistic decision tree. As a result, the network can produce a sequence of decisions that leads to the final classification result, given an input image. The method is trained with soft decisi... | SP:142a01056d20ddab91353b9d2ec07925f82d10ea |
NBDT: Neural-Backed Decision Tree | 1 INTRODUCTION . Many computer vision applications ( e.g . medical imaging and autonomous driving ) require insight into the model ’ s decision process , complicating applications of deep learning which are traditionally black box . Recent efforts in explainable computer vision attempt to address this need and can be g... | Aim to improve the interpretability and the accuracy of the neural network, this paper takes a step further on the integration of NN with a decision tree. It will replace the final linear layer of the NN with a decision tree induced by pre-trained model weights. It takes advantage of both hard and soft decision trees a... | SP:142a01056d20ddab91353b9d2ec07925f82d10ea |
Combining Physics and Machine Learning for Network Flow Estimation | 1 INTRODUCTION . In many applications , ranging from road traffic to supply chains to power networks , the dynamics of flows on edges of a graph is governed by physical laws/models ( Bressan et al. , 2014 ; Garavello & Piccoli , 2006 ) . For instance , the LWR model describes equilibrium equations for road traffic Ligh... | The authors propose a parametric regularizer for estimating unobserved flows in networks, incorporating edge features and other side information. The parameters of the regularizer are learned by means of minimizing the empirical cross-validated MSE. Regularization is necessary because the basic problem, while convex, t... | SP:21296aeb09e1d3d7ca0a729f1ab614f15b12960d |
Combining Physics and Machine Learning for Network Flow Estimation | 1 INTRODUCTION . In many applications , ranging from road traffic to supply chains to power networks , the dynamics of flows on edges of a graph is governed by physical laws/models ( Bressan et al. , 2014 ; Garavello & Piccoli , 2006 ) . For instance , the LWR model describes equilibrium equations for road traffic Ligh... | In this paper, the authors introduce a method for missing flow estimation. These method has potential to address some important applications in transportation, power systems and water management. One major difference compared with the previous work is that edge features are incorporated into the optimization process so... | SP:21296aeb09e1d3d7ca0a729f1ab614f15b12960d |
Learning Efficient Planning-based Rewards for Imitation Learning | 1 INTRODUCTION . Imitation learning ( IL ) offers an alternative to reinforcement learning ( RL ) for training an agent , which mimics the demonstrations of an expert and avoids manually designed reward functions . Behavioral cloning ( BC ) ( Pomerleau , 1991 ) is the simplest form of imitation learning , which learns ... | This paper proposes a method for inverse reinforcement learning that incorporates a differential planning module. Explicit transition dynamics modeling with inverse value iteration is added to promote meaningful reward learning. Empirical evaluations on several high-dimensional Atari environments and 2 continuous contr... | SP:0370e68af5e82fcbde2ca16e57721e455620a1fe |
Learning Efficient Planning-based Rewards for Imitation Learning | 1 INTRODUCTION . Imitation learning ( IL ) offers an alternative to reinforcement learning ( RL ) for training an agent , which mimics the demonstrations of an expert and avoids manually designed reward functions . Behavioral cloning ( BC ) ( Pomerleau , 1991 ) is the simplest form of imitation learning , which learns ... | This paper assumes no access to the reward values and attempts to learn a policy by starting just with one demonstration to define the reward. For obtaining the reward, the authors rely on the ideas from Value Iteration Networks (VIN) method and they add the modules that help to deal with cases with complex transition ... | SP:0370e68af5e82fcbde2ca16e57721e455620a1fe |
Semi-supervised Keypoint Localization | 1 INTRODUCTION . Detecting keypoints helps with fine-grained classification ( Guo & Farrell , 2019 ) and re-identification ( Zhu et al. , 2020 ; Sarfraz et al. , 2018 ) . In the domain of wild animals ( Mathis et al. , 2018 ; Moskvyak et al. , 2020 ; Liu et al. , 2019a ; b ) , annotating data is especially challenging ... | The paper presents an approach to keypoint localization (to retrieve people/animals pose) combining labeled and unlabeled data. Features are extracted and concatenated into a single descriptor per keypoints, by multiplying feature maps and heatmaps and max-pooling over the spatial domain, and used for semantic classifi... | SP:40701460d7ed2175ff193b228f93af7d50911267 |
Semi-supervised Keypoint Localization | 1 INTRODUCTION . Detecting keypoints helps with fine-grained classification ( Guo & Farrell , 2019 ) and re-identification ( Zhu et al. , 2020 ; Sarfraz et al. , 2018 ) . In the domain of wild animals ( Mathis et al. , 2018 ; Moskvyak et al. , 2020 ; Liu et al. , 2019a ; b ) , annotating data is especially challenging ... | This paper presents semi-supervised keypoint localization networks and loss functions to overcome the need for the labeled keypoint data for that task. It simultaneously generates keypoint heatmaps and pose invariant keypoint representations, where these representations were separately used to enforce translation equiv... | SP:40701460d7ed2175ff193b228f93af7d50911267 |
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models | 1 INTRODUCTION . Many modern machine learning algorithms are susceptible to adversarial examples : carefully crafted inputs designed to fool models into giving incorrect outputs ( Biggio et al. , 2013 ; Szegedy et al. , 2014 ; Kurakin et al. , 2016a ; Xie et al. , 2017 ) . Much research has focused on increasing classi... | This work proposes a new form of adversarial training, supported by two proposed adversarial attacks based off a perceptual distance. The choice of perceptual distance (LPIPS), is computed by comparing the activations of (possibly different) two neural networks with respect to a pair of inputs. The authors propose two ... | SP:4815005f4ab4a69abde3b5456b811e4e98ba86c7 |
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models | 1 INTRODUCTION . Many modern machine learning algorithms are susceptible to adversarial examples : carefully crafted inputs designed to fool models into giving incorrect outputs ( Biggio et al. , 2013 ; Szegedy et al. , 2014 ; Kurakin et al. , 2016a ; Xie et al. , 2017 ) . Much research has focused on increasing classi... | This paper studies the adversarial robustness of deep neural networks against multiple and unforeseen threat models. Since there lacks a precise formalization of human perception, this paper adopts LPIPS, a metric that correlates well with human perception based on neural network activations. Then, two adversarial atta... | SP:4815005f4ab4a69abde3b5456b811e4e98ba86c7 |
A Simple Approach To Define Curricula For Training Neural Networks | 1 INTRODUCTION . Stochastic Gradient Descent ( SGD ) ( Robbins & Monro , 1951 ) is a simple yet widely used algorithm for machine learning optimization . There have been many efforts to improve its performance . A number of such directions , such as AdaGrad ( Duchi et al. , 2011 ) , RMSProp ( Tieleman & Hinton , 2012 )... | The paper contains two curriculum learning algorithms of which one assume knowledge of the parameters found by the baseline, uniform-sampling, model to push updates in that direction, and the second orders images according to an increasing stddev/entropy of pixels. While the first approach is impractical because of the... | SP:71d2c08c45a1f4635bb51699e5833c74699731f2 |
A Simple Approach To Define Curricula For Training Neural Networks | 1 INTRODUCTION . Stochastic Gradient Descent ( SGD ) ( Robbins & Monro , 1951 ) is a simple yet widely used algorithm for machine learning optimization . There have been many efforts to improve its performance . A number of such directions , such as AdaGrad ( Duchi et al. , 2011 ) , RMSProp ( Tieleman & Hinton , 2012 )... | This work studies a number of curriculums for faster training of neural networks. They first propose a curriculum named DCL+ that is designed to order data points based on their alignment of gradient with the direction of optimization. This curriculum depends on the evaluation of individual gradients of datapoints as w... | SP:71d2c08c45a1f4635bb51699e5833c74699731f2 |
CURI: A Benchmark for Productive Concept Learning Under Uncertainty | 1 INTRODUCTION . Human concept learning is more flexible than today ’ s AI systems . Human conceptual knowledge is productive : people can understand and generate novel concepts via compositions of existing concepts ( “ an apartment dog ” ) ( Murphy , 2002 ) , unlike standard machine classifiers that are limited to a f... | The following work presents a CLEVR-based compositionality benchmark. The task of the model is to verify logical statements about an image, and in order to achieve such, must learn how to map individual statements to a composition of functions over the image checking for color, placement, shape, etc. Specific to this d... | SP:3f2384e43d16f4b06bf238e4ce097d4e34f25ee7 |
CURI: A Benchmark for Productive Concept Learning Under Uncertainty | 1 INTRODUCTION . Human concept learning is more flexible than today ’ s AI systems . Human conceptual knowledge is productive : people can understand and generate novel concepts via compositions of existing concepts ( “ an apartment dog ” ) ( Murphy , 2002 ) , unlike standard machine classifiers that are limited to a f... | This work proposes the CURI dataset to measure productive concept learning under uncertainty. The dataset is designed using a concept space defined by a language and formulated as a few-shot meta-learning problem to tell apart in-concept samples from out-of-concept samples. The authors also design several out-of-genera... | SP:3f2384e43d16f4b06bf238e4ce097d4e34f25ee7 |
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning | 1 INTRODUCTION . End-to-end neural models have proven to be powerful tools for an expansive set of language and vision problems by effectively emulating the input-output behavior . However , many real problems like Question Answering ( QA ) or Dialog need more interpretable models that can incorporate explicit reasonin... | This paper proposes a neurosymbolic module network that predicts a program structure following a dependency parse, populates that program's arguments, and executes it to answer numerical reasoning questions over text. They claim that compared to Gupta et al. (2020), this approach doesn't require as many domain-specifi... | SP:0a4cf8c20a5ac64540faf909d0e6d3af34e4036c |
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning | 1 INTRODUCTION . End-to-end neural models have proven to be powerful tools for an expansive set of language and vision problems by effectively emulating the input-output behavior . However , many real problems like Question Answering ( QA ) or Dialog need more interpretable models that can incorporate explicit reasonin... | The paper proposes a new model for numerical reasoning in machine comprehension. Given a passage and a query, the model outputs an arithmetic expression over numbers/dates in the passage (e.g. max(23, 26, 42)). The model is trained with weak supervision in the form of numerical answers only. This weak supervision is us... | SP:0a4cf8c20a5ac64540faf909d0e6d3af34e4036c |
LambdaNetworks: Modeling long-range Interactions without Attention | 1 INTRODUCTION . Modeling long-range dependencies in data is a central problem in machine learning . Selfattention ( Bahdanau et al. , 2015 ; Vaswani et al. , 2017 ) has emerged as a popular approach to do so , but the costly memory requirement of self-attention hinders its application to long sequences and multidimens... | This paper proposes a novel lambda layer to capture long-range interactions by transforming available contexts into linear functions, termed lambdas and applying these linear functions to each input separately. The proposed Lambda Network achieves good performances on ImageNet Classification, COCO object detection and ... | SP:28475d91bb10fb0a3a8add77cca7505a839e145d |
LambdaNetworks: Modeling long-range Interactions without Attention | 1 INTRODUCTION . Modeling long-range dependencies in data is a central problem in machine learning . Selfattention ( Bahdanau et al. , 2015 ; Vaswani et al. , 2017 ) has emerged as a popular approach to do so , but the costly memory requirement of self-attention hinders its application to long sequences and multidimens... | This paper presents an efficient method to model long-range interaction. The proposed lambda layer removes the nonlinearity of the original attention operation and makes the matrix multiplication independent of the context, hence skipping expensive computation and storage of large attention maps. Two kinds of lambda fu... | SP:28475d91bb10fb0a3a8add77cca7505a839e145d |
VECoDeR - Variational Embeddings for Community Detection and Node Representation | 1 INTRODUCTION . Graphs are flexible data structures that model complex relationships among entities , i.e . data points as nodes and the relations between nodes via edges . One important task in graph analysis is community detection , where the objective is to cluster nodes into multiple groups ( communities ) . Each ... | The paper deals with the problem of simultaneously learning node embeddings and detecting communities on graphs. Although both tasks are particularly important while analyzing networks, most of the proposed approaches address them independently. The paper proposes a generative model, called VECODER, that aims to jointl... | SP:dc61f3b946fd4ff24d64e8a34483dd2bd0b1b333 |
VECoDeR - Variational Embeddings for Community Detection and Node Representation | 1 INTRODUCTION . Graphs are flexible data structures that model complex relationships among entities , i.e . data points as nodes and the relations between nodes via edges . One important task in graph analysis is community detection , where the objective is to cluster nodes into multiple groups ( communities ) . Each ... | This paper aims to learn node representations of graph to jointly satisfy node embedding properties and community detection property. Node embedding must preserve proximities guaranteeing that adjacent nodes are closer than others. Community detection must promote more similar clustering assignments to adjacent nodes t... | SP:dc61f3b946fd4ff24d64e8a34483dd2bd0b1b333 |
A Probabilistic Approach to Constrained Deep Clustering | 1 INTRODUCTION . The ever-growing amount of data and the time cost associated with its labeling has made clustering a relevant task in the field of machine learning . Yet , in many cases , a fully unsupervised clustering algorithm might naturally find a solution which is not consistent with the domain knowledge ( Basu ... | This paper extends the variational deep embedding VaDE model (a VAE-based clustering method) to integrate pairwise constraints between objects, i.e., must-link and cannot-link. The constraints are integrated a priori as a condition. That is, the prior over the cluster labels is conditioned on the constraints. The whole... | SP:774027f8c53b842fa8ef0569dc1c9b2eaa82872b |
A Probabilistic Approach to Constrained Deep Clustering | 1 INTRODUCTION . The ever-growing amount of data and the time cost associated with its labeling has made clustering a relevant task in the field of machine learning . Yet , in many cases , a fully unsupervised clustering algorithm might naturally find a solution which is not consistent with the domain knowledge ( Basu ... | This work proposes CVaDE which is an extension of variational based deep clustering model (VaDE) with additional incorporation of prior clustering preferences as supervision. These priors guide the underlying clustering process towards a user-desirable partitioning of input data. The priors are provided in the form of ... | SP:774027f8c53b842fa8ef0569dc1c9b2eaa82872b |
On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness | 1 INTRODUCTION . A substantial body of work has shown that deep networks can be highly susceptible to adversarial attacks , in which minor changes to the input lead to incorrect , even bizarre classifications ( Nguyen et al. , 2015 ; Moosavi-Dezfooli et al. , 2016 ; Su et al. , 2019 ; Brendel et al. , 2018 ; Shamir et ... | This paper studies, through a provable approach, whether abstaining (i.e., refusing to answer) can be beneficial for achieving small adversarial/robust error in settings where the input is potentially adversarially perturbed. The paper proves a separation between the power of models with and without abstain. In particu... | SP:95782322a8951193e0690262f6a90d2ed5ed7463 |
On the Power of Abstention and Data-Driven Decision Making for Adversarial Robustness | 1 INTRODUCTION . A substantial body of work has shown that deep networks can be highly susceptible to adversarial attacks , in which minor changes to the input lead to incorrect , even bizarre classifications ( Nguyen et al. , 2015 ; Moosavi-Dezfooli et al. , 2016 ; Su et al. , 2019 ; Brendel et al. , 2018 ; Shamir et ... | This paper proves some fundamental facts about classifiers that can't abstain (provide a non-classification) and their robustness to adversarial perturbations. In Sec. 4, they provide a result that such classifiers are always vulnerable to adversarial perturbations in a technical sense. In particular, there will always... | SP:95782322a8951193e0690262f6a90d2ed5ed7463 |
VEM-GCN: Topology Optimization with Variational EM for Graph Convolutional Networks | 1 INTRODUCTION . Complex graph-structured data are ubiquitous in the real world , ranging from social networks to chemical molecules . Inspired by the remarkable performance of convolutional neural networks ( CNNs ) in processing data with regular grid structures ( e.g. , images ) , a myriad of studies on GCNs have eme... | The authors present a method for tackling the problem of over-smoothing in graph convolutional networks. Specifically, this is achieved by explicitly modelling a latent graph which, ideally, would be a graph which connects an observation to all other observations of the same class and no observations of a different cla... | SP:9977ed83006cd0ccbf385f26220aa9395a723157 |
VEM-GCN: Topology Optimization with Variational EM for Graph Convolutional Networks | 1 INTRODUCTION . Complex graph-structured data are ubiquitous in the real world , ranging from social networks to chemical molecules . Inspired by the remarkable performance of convolutional neural networks ( CNNs ) in processing data with regular grid structures ( e.g. , images ) , a myriad of studies on GCNs have eme... | This paper proposes a method to alleviate the over-smoothing problem of GNNs. The key idea is to generate a latent graph structure via leveraging stochastic block model to approximate the observed graph structure and label information. The learned latent graph is expected to have a clear community structure with dense ... | SP:9977ed83006cd0ccbf385f26220aa9395a723157 |
Zero-Shot Recognition through Image-Guided Semantic Classification | 1 INTRODUCTION . As a feasible solution for addressing the limitations of supervised classification methods , zeroshot learning ( ZSL ) aims to recognize objects whose instances have not been seen during training ( Larochelle et al. , 2008 ; Palatucci et al. , 2009 ) . Unseen classes are recognized by associating seen ... | The authors tackle the problem of zero-shot learning, that is, the recognition of classes and categories for which no visual data are available, but only semantic embedding, providing a description of the classes in terms of auxiliary textual descriptions. To this aim, authors propose a method dubbed Image-Guided Sema... | SP:e0a53b0c2398f49df1c8c053acb1dc4bc64a0729 |
Zero-Shot Recognition through Image-Guided Semantic Classification | 1 INTRODUCTION . As a feasible solution for addressing the limitations of supervised classification methods , zeroshot learning ( ZSL ) aims to recognize objects whose instances have not been seen during training ( Larochelle et al. , 2008 ; Palatucci et al. , 2009 ) . Unseen classes are recognized by associating seen ... | This paper proposes a simple yet effective method for zero-shot learning. In the method, a network is learned to predict the compatibility function weight given the input of the image. The predicted weight is then applied to semantic attributes and the final class label is predicted by the maximum compatibility score. ... | SP:e0a53b0c2398f49df1c8c053acb1dc4bc64a0729 |
Learning Contextual Perturbation Budgets for Training Robust Neural Networks | 1 INTRODUCTION . It has been demonstrated that deep neural networks , although achieving impressive performance on various tasks , are vulnerable to adverarial perturbations ( Szegedy et al. , 2013 ) . Models with high accuracy on clean and unperturbed data can be fooled to have extremely poor performance when input da... | This paper proposes to change the perturbation budget for adversarial attacks to a non-uniform setting where differet input pixels have different perturbation budgets. To achieve this, an additional network is trained to learn the perturbation budget for each part of the input. The approach seems to perform better than... | SP:2997e3ea21f2a8a5dbb7952ecabcc70dfc1e0c57 |
Learning Contextual Perturbation Budgets for Training Robust Neural Networks | 1 INTRODUCTION . It has been demonstrated that deep neural networks , although achieving impressive performance on various tasks , are vulnerable to adverarial perturbations ( Szegedy et al. , 2013 ) . Models with high accuracy on clean and unperturbed data can be fooled to have extremely poor performance when input da... | This paper address the problem of training robust neural networks with non-uniform perturbation budgets on different input pixels. In practice, a perturbation budget generator is introduced to generate the context-aware perturbation budget (i.e. conditioned on the input) for each pixel of the input image. A “robustness... | SP:2997e3ea21f2a8a5dbb7952ecabcc70dfc1e0c57 |
Frequency Regularized Deep Convolutional Dictionary Learning and Application to Blind Denoising | 1 INTRODUCTION . Sparsity in a transform domain is an important and widely applicable property of natural images . This property can be exploited in a variety of tasks such as signal representation , feature extraction , and image processing . For instance , consider restoring an image from a degraded version ( noisy ,... | The paper proposes a new regularization for the dictionary in the learned convolutional sparse coding model of Sreter & Giryes '18. The main contribution is that the dictionary is regularized to be composed of 1) a fixed low-pass filter and 2) a set of learned filters to occupy the complementary high-frequency space. A... | SP:42b2a4961b167d02370a0924d0666be1bf962110 |
Frequency Regularized Deep Convolutional Dictionary Learning and Application to Blind Denoising | 1 INTRODUCTION . Sparsity in a transform domain is an important and widely applicable property of natural images . This property can be exploited in a variety of tasks such as signal representation , feature extraction , and image processing . For instance , consider restoring an image from a degraded version ( noisy ,... | The paper proposes a denoising method with a neural network inspired from convolutional dictionary learning. In the proposed method, one atom of the dictionary is constrained to be a low frequency filters and all other atoms are to be high frequency atoms. The authors also propose to make the threshold depends on the n... | SP:42b2a4961b167d02370a0924d0666be1bf962110 |
Lifelong Learning of Compositional Structures | 1 INTRODUCTION . A major goal of artificial intelligence is to create an agent capable of acquiring a general understanding of the world . Such an agent would require the ability to continually accumulate and build upon its knowledge as it encounters new experiences . Lifelong machine learning addresses this setting , ... | The authors propose a new framework for compositional lifelong learning. In the proposed approach, the composition and adaptation parts are separated when a lifelong learner faces a new task: first, learn the best way to compose all existing components for the new task (and train an optional new component if exiting co... | SP:56eb9cca9680e7ac118f3baf29789f172715c7d0 |
Lifelong Learning of Compositional Structures | 1 INTRODUCTION . A major goal of artificial intelligence is to create an agent capable of acquiring a general understanding of the world . Such an agent would require the ability to continually accumulate and build upon its knowledge as it encounters new experiences . Lifelong machine learning addresses this setting , ... | The paper introduces a framework for lifelong learning of compositional structures. The algorithm is loosely inspired by biological learning and consists of two main steps. The step of component selection relies on existing methods that can learn task-specific structure. In the next step (adaptation), the algorithm ada... | SP:56eb9cca9680e7ac118f3baf29789f172715c7d0 |
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments | 1 INTRODUCTION . Organisms can carve out environmental niches within which they can maintain relative predictability amidst the entropy around them ( Boltzmann , 1886 ; Schrödinger , 1944 ; Schneider & Kay , 1994 ; Friston , 2009 ) . For example , humans go to great lengths to shield themselves from surprise — we band ... | This work proposes an RL approach SMiRL that is able to learn effective policies in unstable environments without the need for external reward. The idea at a high-level is almost the opposite of intrinsic motivation RL approaches, which encourage novelty-seeking behaviors. The proposed method instead aims to minimize s... | SP:0147099ac2866672f507e5e37383fa4f50addd0e |
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments | 1 INTRODUCTION . Organisms can carve out environmental niches within which they can maintain relative predictability amidst the entropy around them ( Boltzmann , 1886 ; Schrödinger , 1944 ; Schneider & Kay , 1994 ; Friston , 2009 ) . For example , humans go to great lengths to shield themselves from surprise — we band ... | The authors target the unsupervised reinforcement learning problem. An opposite idea from the existing approaches by maximizing state entropy is adopted to minimize state entropy. It is interesting that such an idea has achieved good performance in unstable environments. A state distribution is fitted during the intera... | SP:0147099ac2866672f507e5e37383fa4f50addd0e |
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes | 1 INTRODUCTION . Determinantal point processes ( DPPs ) have proven useful for numerous machine learning tasks . For example , recent uses include summarization ( Sharghi et al. , 2018 ) , recommender systems ( Wilhelm et al. , 2018 ) , neural network compression ( Mariet & Sra , 2016 ) , kernel approximation ( Li et a... | Nonsymmetric determinantal point processes (NDPPs) received some attention recently because they allow modeling of both negative and positive correlations between items. This paper developed scalable learning and MAP inference algorithms with space and time complexity linear in ground set size, which is a huge improvem... | SP:eb3a644606a97c248271782c2d9c83e699a329b9 |
Scalable Learning and MAP Inference for Nonsymmetric Determinantal Point Processes | 1 INTRODUCTION . Determinantal point processes ( DPPs ) have proven useful for numerous machine learning tasks . For example , recent uses include summarization ( Sharghi et al. , 2018 ) , recommender systems ( Wilhelm et al. , 2018 ) , neural network compression ( Mariet & Sra , 2016 ) , kernel approximation ( Li et a... | This paper propose a decomposition for non-symmetric determinantal point process (NDPP) kernels (M*M) which reduces the requirements of storage and running to linear in cardinality (M). Additionally, they derive a NDPP maximum a posteriori inference algorithm that applies to both their proposed kernel and the previous ... | SP:eb3a644606a97c248271782c2d9c83e699a329b9 |
Improving Zero-Shot Voice Style Transfer via Disentangled Representation Learning | 1 INTRODUCTION . Style transfer , which automatically converts a data instance into a target style , while preserving its content information , has attracted considerable attention in various machine learning domains , including computer vision ( Gatys et al. , 2016 ; Luan et al. , 2017 ; Huang & Belongie , 2017 ) , vi... | This paper proposes a zero-shot voice style transfer (VST) algorithms that explicitly controls the disentanglement between content information and style information. Experiments show that the proposed algorithm can achieve significant improvement over the existing state-of-the-art VST algorithms. There are two major st... | SP:86d37b08b4c0ab21d139c57bbe3b9e5535eeb3f9 |
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