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Introducing Sample Robustness | 1 INTRODUCTION : . In the age of automated machine learning , we shift our focus evermore towards regarding metahyperparameters such as model-type or training- and validation budget as variables of a loss function in the most abstract sense . For training sets , however , the mere number of samples often determines how... | This work introduces the concept of sample robustness – based on computing the pointwise Lipschitz constant of a data point – and use it to empirically analyze the effects of training on least and most robust training subsets on the performance for different models. This is done for both classification and regression s... | SP:4b6f04ff09138f33a9e9bf6434d1ee1ad04b4daf |
Introducing Sample Robustness | 1 INTRODUCTION : . In the age of automated machine learning , we shift our focus evermore towards regarding metahyperparameters such as model-type or training- and validation budget as variables of a loss function in the most abstract sense . For training sets , however , the mere number of samples often determines how... | The authors introduce sample robustness, a pointwise measure of the sensitivity of the label map to perturbations in the feature space. They do so by taking the pointwise Lipschitz constant of the label map and normalizing it by the label norm. The authors show that training on samples that are / are not sensitive help... | SP:4b6f04ff09138f33a9e9bf6434d1ee1ad04b4daf |
Introducing Sample Robustness | 1 INTRODUCTION : . In the age of automated machine learning , we shift our focus evermore towards regarding metahyperparameters such as model-type or training- and validation budget as variables of a loss function in the most abstract sense . For training sets , however , the mere number of samples often determines how... | This work investigates how to choose the right training set and hyper-parameter for a test set. The authors claim that they introduce a concept of sample robustness based on the Lipschitz constant of the label map. The authors then empirically evaluate the robustness distribution of two datasets and investigate the mod... | SP:4b6f04ff09138f33a9e9bf6434d1ee1ad04b4daf |
Mutual Information State Intrinsic Control | 1 INTRODUCTION . Reinforcement learning ( RL ) allows an agent to learn meaningful skills by interacting with an environment and optimizing some reward function , provided by the environment . Although RL has achieved impressive achievements on various tasks ( Silver et al. , 2017 ; Mnih et al. , 2015 ; Berner et al. ,... | The paper propose MUSIC, an RL algorithm for learning controllers in a unsupervised way. They key idea of the proposed algorithm is to separate the state of the robot like joint angles from the state of the environments such as location of an external object and optimize the mutual information between the two set of st... | SP:611076294a9cdaec02976c2de7df7292fcb88d2b |
Mutual Information State Intrinsic Control | 1 INTRODUCTION . Reinforcement learning ( RL ) allows an agent to learn meaningful skills by interacting with an environment and optimizing some reward function , provided by the environment . Although RL has achieved impressive achievements on various tasks ( Silver et al. , 2017 ; Mnih et al. , 2015 ; Berner et al. ,... | This work introduces MUSIC, a framework for intrinsically motivated RL, where the intrinsic reward comes from maximizing the mutual information between the agent's state and the surrounding environment's state. The authors motivate and describe this approach, explain its incorporation into various training modes, exhau... | SP:611076294a9cdaec02976c2de7df7292fcb88d2b |
Mutual Information State Intrinsic Control | 1 INTRODUCTION . Reinforcement learning ( RL ) allows an agent to learn meaningful skills by interacting with an environment and optimizing some reward function , provided by the environment . Although RL has achieved impressive achievements on various tasks ( Silver et al. , 2017 ; Mnih et al. , 2015 ; Berner et al. ,... | This paper proposes the use of state control as intrinsic motivation. It does so by separating the overall state into an agent state and a surrounding state. The idea then is to maximize the mutual information between the agent's internal state and the environment state. This mutual information is given as a reward to ... | SP:611076294a9cdaec02976c2de7df7292fcb88d2b |
Jumpy Recurrent Neural Networks | 1 INTRODUCTION . It is said that change happens slowly and then all at once . Billiards balls move across a table before colliding and changing trajectories ; water molecules cool slowly and then undergo a rapid phase transition into ice ; and economic systems enjoy periods of stability interspersed with abrupt market ... | This paper proposes a recurrent network architecture for future prediction where the hidden states (and the outputs) aren't updated step by step as done traditionally. Instead, the network models the hidden state dynamics as being piecewise linear over varying time spans. It learns to produce the linear dynamics togeth... | SP:2a2f4f5c8cb0759b0c16a577171313d9499f969b |
Jumpy Recurrent Neural Networks | 1 INTRODUCTION . It is said that change happens slowly and then all at once . Billiards balls move across a table before colliding and changing trajectories ; water molecules cool slowly and then undergo a rapid phase transition into ice ; and economic systems enjoy periods of stability interspersed with abrupt market ... | This work presents Jumpy RNNs, a recurrent network that learns to take variable length steps based on time-scales of the data. The core idea of the paper is to learn a hidden velocity and time span, along with the standard hidden state. The hidden velocity is then used to linearly interpolate the hidden state within th... | SP:2a2f4f5c8cb0759b0c16a577171313d9499f969b |
Jumpy Recurrent Neural Networks | 1 INTRODUCTION . It is said that change happens slowly and then all at once . Billiards balls move across a table before colliding and changing trajectories ; water molecules cool slowly and then undergo a rapid phase transition into ice ; and economic systems enjoy periods of stability interspersed with abrupt market ... | This paper proposes Jumpy Recurrent Neural Network, an RNN model with non-uniform time steps. To train this model, the authors propose to use a greedy supervision to determine optimal time intervals. The experiments on linear dynamics prediction and planning show comparable performance of proposed model against standar... | SP:2a2f4f5c8cb0759b0c16a577171313d9499f969b |
On the Latent Space of Flow-based Models | 1 INTRODUCTION . Normalizing flows ( Rezende and Mohamed , 2015 ; Kobyzev et al. , 2020 ) have shown considerable potential for the tasks of modelling and inferring expressive distributions through the learning of well-specified probabilistic models . Contemporary flow-based approaches define a latent space with dimens... | This paper proposes a new method to train flow models on data from low dimensional manifolds embedded in high dimensional ambient spaces. The basic idea is based on minimizing the KL divergence in the latent space, which is equivalent to maximizing expected log-likelihood over the data distribution. Since the KL betwee... | SP:74b9886e22662058748cc0cde4ba88f29f340f55 |
On the Latent Space of Flow-based Models | 1 INTRODUCTION . Normalizing flows ( Rezende and Mohamed , 2015 ; Kobyzev et al. , 2020 ) have shown considerable potential for the tasks of modelling and inferring expressive distributions through the learning of well-specified probabilistic models . Contemporary flow-based approaches define a latent space with dimens... | This paper proposes a modification to the latent distribution of a flow model, replacing the commonly used full-rank Normal with a low-rank one which has the form N(0, AA^T). To train this degenerate model, the spread divergence from Zhang et al. (2020) is used and some approximation is made (e.g., ignoring the first e... | SP:74b9886e22662058748cc0cde4ba88f29f340f55 |
On the Latent Space of Flow-based Models | 1 INTRODUCTION . Normalizing flows ( Rezende and Mohamed , 2015 ; Kobyzev et al. , 2020 ) have shown considerable potential for the tasks of modelling and inferring expressive distributions through the learning of well-specified probabilistic models . Contemporary flow-based approaches define a latent space with dimens... | This paper proposes a new method of training flow models, instead of minimizing KL divergence in the data space X, the paper proposes to minimize the KL divergence in the latent space Z. However, the problem of this is dimension mismatch, so the KL divergence is ill-defined. The proposed solution is to add noise, such ... | SP:74b9886e22662058748cc0cde4ba88f29f340f55 |
CT-Net: Channel Tensorization Network for Video Classification | 3D convolution is powerful for video classification but often computationally expensive , recent studies mainly focus on decomposing it on spatial-temporal and/or channel dimensions . Unfortunately , most approaches fail to achieve a preferable balance between convolutional efficiency and feature-interaction sufficienc... | The paper proposes a new architecture for lightweight action classification networks, named Channel Tensorization Network (CT-Net). The idea of this architecture is the tensorization of mid-level input features in combination with an attention mechanism that allows to select relevant features. the channel tensorization... | SP:022d5186d69b73d9083ab192b5dd240a1cc61406 |
CT-Net: Channel Tensorization Network for Video Classification | 3D convolution is powerful for video classification but often computationally expensive , recent studies mainly focus on decomposing it on spatial-temporal and/or channel dimensions . Unfortunately , most approaches fail to achieve a preferable balance between convolutional efficiency and feature-interaction sufficienc... | This manuscript proposes a novel convolutional operation for learning representations from video data. By decomposing the channel dimension into sub dimensions in the typically 4D video data (Time, Channel, Width, Height), one defines spatial-temporal separable convolution for each sub-dimension. This could improve the... | SP:022d5186d69b73d9083ab192b5dd240a1cc61406 |
CT-Net: Channel Tensorization Network for Video Classification | 3D convolution is powerful for video classification but often computationally expensive , recent studies mainly focus on decomposing it on spatial-temporal and/or channel dimensions . Unfortunately , most approaches fail to achieve a preferable balance between convolutional efficiency and feature-interaction sufficienc... | This paper presents a new CNN module to learn video feature representations for action recognition, with a particular focus on increasing channel interactions for spatio-temporal modeling. To achieve that, the authors propose to divide feature channels into several sub-dimensions (called channel tensorization) and then... | SP:022d5186d69b73d9083ab192b5dd240a1cc61406 |
Deformable DETR: Deformable Transformers for End-to-End Object Detection | DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance . However , it suffers from slow convergence and limited feature spatial resolution , due to the limitation of Transformer attention modules in processing image feature maps .... | This paper aims to improve a very recent detection model -- DETR, which suffers from two issues: long training time and limited feature spatial resolution. Targeting these issues, this paper proposes (1) deformable attention (2) multi-scale processing (inputs/attention) for DETR, which have greatly reduced the trainin... | SP:5c52d6d7c8754492dbfe1d786a7f15b76390c1fa |
Deformable DETR: Deformable Transformers for End-to-End Object Detection | DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance . However , it suffers from slow convergence and limited feature spatial resolution , due to the limitation of Transformer attention modules in processing image feature maps .... | The main contribution is a new attention module called deformable attention module. Like deformable convolution, it adds a translation term into the expression of the transformer, allowing a sparse spatial sampling. The resulting model is very interesting in terms of convergence and complexity compared to the original ... | SP:5c52d6d7c8754492dbfe1d786a7f15b76390c1fa |
Deformable DETR: Deformable Transformers for End-to-End Object Detection | DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance . However , it suffers from slow convergence and limited feature spatial resolution , due to the limitation of Transformer attention modules in processing image feature maps .... | As a new framework for object detection, DETR is very important. However, it suffers from slow convergence and limited feature spatial resolution. This paper proposes deformable attention, which attends to a small set of sampling locations rather than all the locations in the original DETR. Besides, the paper applies ... | SP:5c52d6d7c8754492dbfe1d786a7f15b76390c1fa |
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning | 1 INTRODUCTION . Reinforcement Learning ( RL ) is an attractive paradigm for robotic learning because of its flexibility in being able to learn a diverse range of skills and its capacity to continuously improve . However , RL algorithms typically require a large amount of data to solve each individual task , including ... | This paper proposes PARROT, a method for learning a policy prior from a dataset of expert state-action pairs that have been derived from multiple similar tasks. The policy prior is parameterized as a deep conditional generative model that maps a noise input and a state to an action. The latter map can be inverted, whic... | SP:91db871c227488ddc9fedf066795935bd6472e18 |
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning | 1 INTRODUCTION . Reinforcement Learning ( RL ) is an attractive paradigm for robotic learning because of its flexibility in being able to learn a diverse range of skills and its capacity to continuously improve . However , RL algorithms typically require a large amount of data to solve each individual task , including ... | This work proposes a method, PARROT, to learn data-driven priors for deep reinforcement learning agents. Motivated by the idea of pre-training with existing data of similar tasks, the authors propose to learn state-conditional behavioral priors from a set of similar tasks for reinforcement learning agents, such that a ... | SP:91db871c227488ddc9fedf066795935bd6472e18 |
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning | 1 INTRODUCTION . Reinforcement Learning ( RL ) is an attractive paradigm for robotic learning because of its flexibility in being able to learn a diverse range of skills and its capacity to continuously improve . However , RL algorithms typically require a large amount of data to solve each individual task , including ... | This paper introduces PARROT, a novel approach for pretraining a reinforcement learning agent on near-optimal trajectories by learning a behavioral prior. Essentially, the authors learn a word2vec style embedding of actions for a simple virtual single-arm environment. This embedding will naturally place more common exa... | SP:91db871c227488ddc9fedf066795935bd6472e18 |
On Alignment in Deep Linear Neural Networks | 1 INTRODUCTION . Although overparameterized deep networks can interpolate randomly labeled training data ( Du et al. , 2019 ; Wu et al. , 2019 ) , training overparameterized networks with modern optimizers often leads to solutions that generalize well . This suggests that there is a form of implicit regularization occu... | This article extends the notion of alignment [Ji and Telgarsky, 2018] to linear neural networks with multiple output nodes, which requires the consecutive layers (i+1, i) to have the same (right, left) singular spaces. The authors identify necessary and sufficient conditions under which, alignment is an invariant of th... | SP:d3d113911e8ca3b6c36942c593c1f2cbffe6deb7 |
On Alignment in Deep Linear Neural Networks | 1 INTRODUCTION . Although overparameterized deep networks can interpolate randomly labeled training data ( Du et al. , 2019 ; Wu et al. , 2019 ) , training overparameterized networks with modern optimizers often leads to solutions that generalize well . This suggests that there is a form of implicit regularization occu... | This paper considers deep linear networks trained by gradient descent with the squared loss, and characterizes when alignment happens, meaning that for any pair of adjacent weight matrices W_{i+1} and W_i, the (unsorted, signed) right singular vectors of W_{i+1} are identical to the (unsorted, signed) left singular vec... | SP:d3d113911e8ca3b6c36942c593c1f2cbffe6deb7 |
On Alignment in Deep Linear Neural Networks | 1 INTRODUCTION . Although overparameterized deep networks can interpolate randomly labeled training data ( Du et al. , 2019 ; Wu et al. , 2019 ) , training overparameterized networks with modern optimizers often leads to solutions that generalize well . This suggests that there is a form of implicit regularization occu... | The paper presents an extension of the idea of alignment in linear neural networks, that can help in providing convergence analysis of such networks. Such a notion was previously studied for networks with a single output. The current paper extends it to networks with multi-dimensional outputs. The paper offers multiple... | SP:d3d113911e8ca3b6c36942c593c1f2cbffe6deb7 |
Are all negatives created equal in contrastive instance discrimination? | 1 INTRODUCTION . In recent years , there has been tremendous progress on self-supervised learning ( SSL ) , a paradigm in which representations are learned using a pre-training task that uses only unlabeled data . These representations are then used on downstream tasks , such as classification or object detection . Sin... | This paper argues that in contrastive self-supervised learning, different negative instances have different importance. This importance is relevant to the ``difficulty" of negative instances. On ImageNet and MoCo2, the authors show that using the most difficult 5% negative instances can achieve similar performance comp... | SP:857aa1392e6039808d27889d932d82ad756a12b6 |
Are all negatives created equal in contrastive instance discrimination? | 1 INTRODUCTION . In recent years , there has been tremendous progress on self-supervised learning ( SSL ) , a paradigm in which representations are learned using a pre-training task that uses only unlabeled data . These representations are then used on downstream tasks , such as classification or object detection . Sin... | In this paper, the authors carried out a series of experiments to analyze the impact of negative samples in contrastive learning (instance discrimination - CID). In particular, they try to identify which difficulty range is important for representation learning. Of the many recent self-supervised learning approaches, t... | SP:857aa1392e6039808d27889d932d82ad756a12b6 |
Are all negatives created equal in contrastive instance discrimination? | 1 INTRODUCTION . In recent years , there has been tremendous progress on self-supervised learning ( SSL ) , a paradigm in which representations are learned using a pre-training task that uses only unlabeled data . These representations are then used on downstream tasks , such as classification or object detection . Sin... | This paper mainly studied how the negative samples can affect the model performance in supervised learning CIO works. Through the experiments, this work has a few interesting findings, including the majority of negative samples are not important for the model learning, only a small subset of hard samples determine the ... | SP:857aa1392e6039808d27889d932d82ad756a12b6 |
Cross-model Back-translated Distillation for Unsupervised Machine Translation | 1 INTRODUCTION . Machine translation ( MT ) is a core task in natural language processing that involves both language understanding and generation . Recent neural approaches ( Vaswani et al. , 2017 ; Wu et al. , 2019 ) have advanced the state of the art with near human-level performance ( Hassan et al. , 2018 ) . Howev... | In this paper, two unsupervised agents are utilized at cross-model by using the dual nature of the unsupervised machine translation model, in which forward translation of agent_1 is combined with the backward translation of agent_2, more synthetic translation pairs are obtained to train a new supervised machine transla... | SP:da47e8e2ec5533446d5f341b7e12afec02412473 |
Cross-model Back-translated Distillation for Unsupervised Machine Translation | 1 INTRODUCTION . Machine translation ( MT ) is a core task in natural language processing that involves both language understanding and generation . Recent neural approaches ( Vaswani et al. , 2017 ; Wu et al. , 2019 ) have advanced the state of the art with near human-level performance ( Hassan et al. , 2018 ) . Howev... | The paper proposes an additional stage of training for unsupervised NMT models utilizing synthetic data generated from multiple independently trained models. The generated synthetic data uses two stages of back-translation, with different models, in order to "diversify" the set of training data used for fine-tuning the... | SP:da47e8e2ec5533446d5f341b7e12afec02412473 |
Cross-model Back-translated Distillation for Unsupervised Machine Translation | 1 INTRODUCTION . Machine translation ( MT ) is a core task in natural language processing that involves both language understanding and generation . Recent neural approaches ( Vaswani et al. , 2017 ; Wu et al. , 2019 ) have advanced the state of the art with near human-level performance ( Hassan et al. , 2018 ) . Howev... | This paper describes a method to enhance unsupervised machine translation through data augmentation. The idea is pretty straight-forward, if not altogether intuitive, you begin by training two bidirectional (i.e.: they can translate source to target and target to source) unsupervised MT systems A and B. The tested scen... | SP:da47e8e2ec5533446d5f341b7e12afec02412473 |
A Critique of Self-Expressive Deep Subspace Clustering | 1 INTRODUCTION AND BACKGROUND . Subspace clustering is a classical unsupervised learning problem , where one wishes to segment a given dataset into a prescribed number of clusters , and each cluster is defined as a linear ( or affine ) subspace with dimension lower than the ambient space . There have been a wide variet... | This paper studies the flaws associated with extending subspace clustering methods to the nonlinear manifolds scenario. In particular, the authors demonstrate that the optimization problem solved due to the extension can be ill-posed and thus lead to solutions which are degenerate/trivial in nature. The paper also show... | SP:6ceb9378033da5eab4af5ebe84e1f9f5b99b4c38 |
A Critique of Self-Expressive Deep Subspace Clustering | 1 INTRODUCTION AND BACKGROUND . Subspace clustering is a classical unsupervised learning problem , where one wishes to segment a given dataset into a prescribed number of clusters , and each cluster is defined as a linear ( or affine ) subspace with dimension lower than the ambient space . There have been a wide variet... | This paper critiques the commonly-used self-expressive cost function used to learn embeddings for deep subspace clustering. The authors point out that the empirical improvements obtained by deep self-expressive subspace clustering may be artifacts of post processing on the learned affinity matrix. They then theoretical... | SP:6ceb9378033da5eab4af5ebe84e1f9f5b99b4c38 |
A Critique of Self-Expressive Deep Subspace Clustering | 1 INTRODUCTION AND BACKGROUND . Subspace clustering is a classical unsupervised learning problem , where one wishes to segment a given dataset into a prescribed number of clusters , and each cluster is defined as a linear ( or affine ) subspace with dimension lower than the ambient space . There have been a wide variet... | The paper calls into question the significance of previous results on Self-Expressive Deep Subspace Clustering (SEDSC) models, which are touted as successful extensions of the linear subspace clustering (using the self-expressive property) to non-linear data structures. The authors present a set of theoretical results ... | SP:6ceb9378033da5eab4af5ebe84e1f9f5b99b4c38 |
Revisiting the Stability of Stochastic Gradient Descent: A Tightness Analysis | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) has gained great popularity in solving machine learning optimization problems ( Kingma & Ba , 2014 ; Johnson & Zhang , 2013 ) . SGD leverages the finite-sum structure of the objective function , avoids the expensive computation of exact gradients , and thus provides ... | This paper considers the stability of the stochastic gradient decent algorithm under different conditions. They show a lower bound for the stability of SGD in the smooth and convex case, and show that the bound can be tightened for linear models. They give a tight bound for the stability of SGD with decreasing step siz... | SP:27d27fbdba1259a29d0c538ebb873c444a6a4c11 |
Revisiting the Stability of Stochastic Gradient Descent: A Tightness Analysis | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) has gained great popularity in solving machine learning optimization problems ( Kingma & Ba , 2014 ; Johnson & Zhang , 2013 ) . SGD leverages the finite-sum structure of the objective function , avoids the expensive computation of exact gradients , and thus provides ... | This paper studies stability of SGD which is a popular optimization algorithm. The authors aim to build a tight stability analysis. In particular, the authors show by constructing specific problems that the existing stability bounds for SGD applied to convex problems are tight within a constant factor. Then, the author... | SP:27d27fbdba1259a29d0c538ebb873c444a6a4c11 |
Revisiting the Stability of Stochastic Gradient Descent: A Tightness Analysis | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) has gained great popularity in solving machine learning optimization problems ( Kingma & Ba , 2014 ; Johnson & Zhang , 2013 ) . SGD leverages the finite-sum structure of the objective function , avoids the expensive computation of exact gradients , and thus provides ... | The paper studies the stability of stochastic gradient descent (SGD), which is one of the framework used for explaining generalization. More specifically, the paper investigates the tightness of the algorithmic stability bounds for SGD given by Hardt et al. (2016). Furthermore, the authors propose the Hessian contracti... | SP:27d27fbdba1259a29d0c538ebb873c444a6a4c11 |
The impacts of known and unknown demonstrator irrationality on reward inference | 1 INTRODUCTION . Motivated by difficulty in reward specification ( Lehman et al. , 2018 ) , inverse reinforcement learning ( IRL ) methods estimate a reward function from human demonstrations ( Ng et al. , 2000 ; Abbeel and Ng , 2004 ; Kalman , 1964 ; Jameson and Kreindler , 1973 ; Mombaur et al. , 2010 ) . The central... | This paper investigates the effect different irrationality types have on reward inference. The irrationality types are modelled in the context of an Uncertain-Reward Markov Decision Process (URMDP) that is similar to an ordinary MDP but with a prior distribution over reward functions. Different irrationality types are ... | SP:c785f992a8e83a23d07148729b18a37e1c294f6d |
The impacts of known and unknown demonstrator irrationality on reward inference | 1 INTRODUCTION . Motivated by difficulty in reward specification ( Lehman et al. , 2018 ) , inverse reinforcement learning ( IRL ) methods estimate a reward function from human demonstrations ( Ng et al. , 2000 ; Abbeel and Ng , 2004 ; Kalman , 1964 ; Jameson and Kreindler , 1973 ; Mombaur et al. , 2010 ) . The central... | This work studies the effect of modeling systematic irrationality of the demonstrator for reward learning problems. By manipulating different factors of Bellman update, the authors simulate different irrational behavior in demonstrations. Experiments in gridworld and a 2D driving domain demonstrate that modeling irrati... | SP:c785f992a8e83a23d07148729b18a37e1c294f6d |
The impacts of known and unknown demonstrator irrationality on reward inference | 1 INTRODUCTION . Motivated by difficulty in reward specification ( Lehman et al. , 2018 ) , inverse reinforcement learning ( IRL ) methods estimate a reward function from human demonstrations ( Ng et al. , 2000 ; Abbeel and Ng , 2004 ; Kalman , 1964 ; Jameson and Kreindler , 1973 ; Mombaur et al. , 2010 ) . The central... | This paper proposed modifications to the Bellman equation to capture known human irrationalities and showed that the reward under some conditions (the type of irrationality and parameter settings) can be better inferred compared to a rational agent. The authors demonstrated this through simulations in three different e... | SP:c785f992a8e83a23d07148729b18a37e1c294f6d |
Overinterpretation reveals image classification model pathologies | 1 Introduction . Well-founded decisions by machine learning ( ML ) systems are critical for high-stakes applications such as autonomous vehicles and medical diagnosis . Pathologies in models and their respective training datasets can result in unintended behavior during deployment if the systems are confronted with nov... | This work reports the problem of image classification datasets (CIFAR-10 and ImageNet) which contains statistical patterns present in both training and tests that can be leveraged by neural networks to achieve high accuracy, but would not be discerned as salient features by humans. Using Sufficient Input Subsets (SIS),... | SP:2cf4a3964537ff5dd1f7b600ab567b4d0b3cc03e |
Overinterpretation reveals image classification model pathologies | 1 Introduction . Well-founded decisions by machine learning ( ML ) systems are critical for high-stakes applications such as autonomous vehicles and medical diagnosis . Pathologies in models and their respective training datasets can result in unintended behavior during deployment if the systems are confronted with nov... | This paper proposes "overinterpretation" which describes the phenomenon that CNNs could achieve high test accuracy while replying on features that lack semantic meaning. To demonstrate overinterpretation on CIFAR-10 and ImageNet, the authors use Batched Gradient SIS to select a small subset of pixels for each image and... | SP:2cf4a3964537ff5dd1f7b600ab567b4d0b3cc03e |
Overinterpretation reveals image classification model pathologies | 1 Introduction . Well-founded decisions by machine learning ( ML ) systems are critical for high-stakes applications such as autonomous vehicles and medical diagnosis . Pathologies in models and their respective training datasets can result in unintended behavior during deployment if the systems are confronted with nov... | The work utilizes the SIS (a local feature-importance method) to empirically prove that on existing benchmark datasets, the trained convnets are capable of making decisions based on a very small subset of pixels that are meaningless to the human observer but are nonetheless strong signals. Interestingly, unlike common ... | SP:2cf4a3964537ff5dd1f7b600ab567b4d0b3cc03e |
REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning | 1 INTRODUCTION . Most reinforcement learning methods train an agent from scratch , typically requiring a huge amount of time and computing resources . Accelerating the learning processes for complex tasks has been one of the most challenging problems in reinforcement learning ( Kaelbling et al. , 1996 ; Sutton & Barto ... | This paper proposes a new transfer learning approach to leverage the previously learned knowledge stored in a pre-trained teacher policy to facilitate the learning in a new task. The proposed method combines an established technique termed kickstarted training with a simple experience filtering method. The kickstarted ... | SP:86d5bb0c199f89e0ba8cb44a30199400add61308 |
REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning | 1 INTRODUCTION . Most reinforcement learning methods train an agent from scratch , typically requiring a huge amount of time and computing resources . Accelerating the learning processes for complex tasks has been one of the most challenging problems in reinforcement learning ( Kaelbling et al. , 1996 ; Sutton & Barto ... | The proposed method relies on kickstarting, thus using policy distillation as an auxiliary loss for transferring from a source task to a target task, as a starting point. In addition, the authors add 'instance transfer', i.e., selecting some prioritized data from the source task to be used to train the target task. The... | SP:86d5bb0c199f89e0ba8cb44a30199400add61308 |
REPAINT: Knowledge Transfer in Deep Actor-Critic Reinforcement Learning | 1 INTRODUCTION . Most reinforcement learning methods train an agent from scratch , typically requiring a huge amount of time and computing resources . Accelerating the learning processes for complex tasks has been one of the most challenging problems in reinforcement learning ( Kaelbling et al. , 1996 ; Sutton & Barto ... | This paper deals with transfer learning in RL. The problem broadly defined is to improve performance of an agent on a new task, given an agent (teacher) trained on a previous (different) task. There are multiple approaches to this from fine-tuning, distillation to instance transfer. A central question in this field is ... | SP:86d5bb0c199f89e0ba8cb44a30199400add61308 |
Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles | 1 INTRODUCTION . The ability to learn complex behaviors through interaction will enable the autonomous deployment of various robotic systems in the real world . Reinforcement learning ( RL ) provides a key framework for realizing these capabilities , but efficiency of the learning process remains a prevalent concern . ... | The paper proposes LOVE, an adaptation of DOVE (Seyde’20) to latent variable predictive models (Seyde’20 only condsidered predictive models without latent variables). Seyde’20 proposes to use a generalization of Upper Confidence Bound to deep model-based RL, by training an ensemble of models and value functions, and tr... | SP:e5629c2dd2fcd0113b302aabd5618a64a20fcdfa |
Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles | 1 INTRODUCTION . The ability to learn complex behaviors through interaction will enable the autonomous deployment of various robotic systems in the real world . Reinforcement learning ( RL ) provides a key framework for realizing these capabilities , but efficiency of the learning process remains a prevalent concern . ... | This paper proposes LOVE (Latent Optimistic Value Exploration), a model-based exploration algorithm for POMDP or pixel-based control systems. The method builds upon Dreamer (Hafner et al. 2019) for learning latent models, and the variance of value estimates (one transition/reward/value model per particle) estimates the... | SP:e5629c2dd2fcd0113b302aabd5618a64a20fcdfa |
Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles | 1 INTRODUCTION . The ability to learn complex behaviors through interaction will enable the autonomous deployment of various robotic systems in the real world . Reinforcement learning ( RL ) provides a key framework for realizing these capabilities , but efficiency of the learning process remains a prevalent concern . ... | The authors proposed latent optimistic value exploration (LOVE) as a mechanism to leverage optimistic exploration for continuous visual control. The main idea is to use a small (~5) ensemble of latent models with shared encoders (and therefore shared learned latent space) but different transition, reward and value mode... | SP:e5629c2dd2fcd0113b302aabd5618a64a20fcdfa |
Hidden Incentives for Auto-Induced Distributional Shift | 1 INTRODUCTION . Consider a content recommendation system whose performance is measured by accuracy of predicting what users will click . This system can achieve better performance by either 1 ) making better predictions , or 2 ) changing the distribution of users such that predictions are easier to make . We propose t... | This paper introduces the concept of auto-induced distributional shift (ADS), and argues that some meta learning and reinforcement learning algorithms have the incentives to change the distribution so that the problem is easier to solve. The paper presents unit tests to detect hidden incentives for auto-induced distrib... | SP:d1f5b4cfa8dd66dbaef07852b1832fad22189d54 |
Hidden Incentives for Auto-Induced Distributional Shift | 1 INTRODUCTION . Consider a content recommendation system whose performance is measured by accuracy of predicting what users will click . This system can achieve better performance by either 1 ) making better predictions , or 2 ) changing the distribution of users such that predictions are easier to make . We propose t... | Abstract: The paper highlights an interesting problem of learning dynamics, where a learning system has the incentives to change it’s future input in order to increase its performance. This is not always problematic, but it can, at times, lead to perverse incentives for the system, such as under-performing on users wit... | SP:d1f5b4cfa8dd66dbaef07852b1832fad22189d54 |
Hidden Incentives for Auto-Induced Distributional Shift | 1 INTRODUCTION . Consider a content recommendation system whose performance is measured by accuracy of predicting what users will click . This system can achieve better performance by either 1 ) making better predictions , or 2 ) changing the distribution of users such that predictions are easier to make . We propose t... | This paper discusses a phenomenon where machine-learned models may influence user behaviors in future iterations, creating self-selection effects such as filter bubbles or propagation of fake news. The paper calls these effects auto-induced distribution shift (ADS) and argues that a specific meta-learning algorithm PBT... | SP:d1f5b4cfa8dd66dbaef07852b1832fad22189d54 |
An Attention Free Transformer | We introduce Attention Free Transformer ( AFT ) , an efficient variant of Transformers ( Vaswani et al. , 2017 ) that eliminates the need for dot product attention . AFT offers great simplicity and efficiency compared with standard Transformers , where the multi-head attention operation is replaced with the composition... | The paper introduces a method to replace qkv-attention by a simpler, efficient building block. This is done by element-wise multiplication of a query representation with a compressed kv-memory. Per-channel attention pooling is used to compress the kv-memory. The model is derived from a softmax-free version of self-atte... | SP:66b1c09b1f72ea9699c8fb738b9996d06a84e1aa |
An Attention Free Transformer | We introduce Attention Free Transformer ( AFT ) , an efficient variant of Transformers ( Vaswani et al. , 2017 ) that eliminates the need for dot product attention . AFT offers great simplicity and efficiency compared with standard Transformers , where the multi-head attention operation is replaced with the composition... | The paper suggest an alternative to the Multi-Head Attention (MHA) operation, which is one of the core elements in Transformers models. The proposed alternative is targeting the non-linear soft-max operator (in the MHA) and suggest to replace it with the "relu" operator. After doing so they could reformulate the new a... | SP:66b1c09b1f72ea9699c8fb738b9996d06a84e1aa |
An Attention Free Transformer | We introduce Attention Free Transformer ( AFT ) , an efficient variant of Transformers ( Vaswani et al. , 2017 ) that eliminates the need for dot product attention . AFT offers great simplicity and efficiency compared with standard Transformers , where the multi-head attention operation is replaced with the composition... | This paper proposes an efficient transformer variant by replacing softmax in the self-attention layer with a RELU activation and arranging the computation using element-wise products and global/local pooling. This reduces complexity to linear complexity in the non-autoregressive case and log-linear complexity in the au... | SP:66b1c09b1f72ea9699c8fb738b9996d06a84e1aa |
Causal Inference Q-Network: Toward Resilient Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( DRL ) methods have shown enhanced performance , gained widespread applications ( Mnih et al. , 2015 ; 2016 ; Ecoffet et al. , 2019 ; Silver et al. , 2017 ; Mao et al. , 2017 ) , and improved robot learning ( Gu et al. , 2017 ) in navigation systems ( Tai et al. , 2017 ; Na... | The paper presents a framework for deep reinforcement learning that is motivated by causal inference and with the central objective of being resilient to observational interferences. The key idea is to use interference labels in the training phase to learn a causal model including a hidden confounding state, and then u... | SP:f359fc20c6359739c048c5852909a9655925e187 |
Causal Inference Q-Network: Toward Resilient Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( DRL ) methods have shown enhanced performance , gained widespread applications ( Mnih et al. , 2015 ; 2016 ; Ecoffet et al. , 2019 ; Silver et al. , 2017 ; Mao et al. , 2017 ) , and improved robot learning ( Gu et al. , 2017 ) in navigation systems ( Tai et al. , 2017 ; Na... | This paper proposes a method, the Causal Inference Q-Network (CIQ), for training deep RL agents that are robust to abrupt interferences in observations, such as frame blackouts, Gaussian noise or adversarial perturbations. During training time, a binary interference label is provided to the agent at each time step indi... | SP:f359fc20c6359739c048c5852909a9655925e187 |
Causal Inference Q-Network: Toward Resilient Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( DRL ) methods have shown enhanced performance , gained widespread applications ( Mnih et al. , 2015 ; 2016 ; Ecoffet et al. , 2019 ; Silver et al. , 2017 ; Mao et al. , 2017 ) , and improved robot learning ( Gu et al. , 2017 ) in navigation systems ( Tai et al. , 2017 ; Na... | Overview: The paper introduces a causal mechanism that both creates and explains away noise interventions into observational data fed into RL agents. The authors propose a form of resilient agent, that based on training data containing labeled interventions, learns both Q function and the causal impact of interventions... | SP:f359fc20c6359739c048c5852909a9655925e187 |
A law of robustness for two-layers neural networks | √ n/k where n is the number of datapoints . In particular , this conjecture implies that overparametrization is necessary for robustness , since it means that one needs roughly one neuron per datapoint to ensure a O ( 1 ) -Lipschitz network , while mere data fitting of d-dimensional data requires only one neuron per d ... | This submission studies the relationship between the hidden-layer size of a two-layer neural network and its robustness, that is measured by its Lipschitz constant here. This paper first makes a conjecture that any two-layer neural network with k-neurons and Lipschitz activation functions that perfectly fit the data mu... | SP:4d996de760a12de3340883269c88cb7b31268ca7 |
A law of robustness for two-layers neural networks | √ n/k where n is the number of datapoints . In particular , this conjecture implies that overparametrization is necessary for robustness , since it means that one needs roughly one neuron per datapoint to ensure a O ( 1 ) -Lipschitz network , while mere data fitting of d-dimensional data requires only one neuron per d ... | In this article, the authors investigated the fundamental trade-off between the size of a neural network and its robustness (measured by its Lipschitz constant), in the setting of a single-hidden-layer network with $k$ neurons and (approximately) Gaussian data, by proposing two conjectures, Conjecture 1 and 2, on the (... | SP:4d996de760a12de3340883269c88cb7b31268ca7 |
A law of robustness for two-layers neural networks | √ n/k where n is the number of datapoints . In particular , this conjecture implies that overparametrization is necessary for robustness , since it means that one needs roughly one neuron per datapoint to ensure a O ( 1 ) -Lipschitz network , while mere data fitting of d-dimensional data requires only one neuron per d ... | This paper concerns the Lipschitz constant of two-layer neural networks that fit "generic data sets" exactly. A key contribution is the statement of two conjectures relating the number of hidden neurons in the network to the Lipschitz constant. Roughly, to have $O(1)$ Lipschitz constant, the authors conjecture the nu... | SP:4d996de760a12de3340883269c88cb7b31268ca7 |
Taking Notes on the Fly Helps Language Pre-Training | 1 INTRODUCTION . Unsupervised language pre-training , e.g. , BERT ( Devlin et al. , 2018 ) , is shown to be a successful way to improve the performance of various NLP downstream tasks . However , as the pre-training task requires no human labeling effort , a massive scale of training corpus from the Web can be used to ... | This paper proposes Taking Notes on the Fly, a technique to improve the training efficiency of language-modeling style pretraining. It works by identifying rare words in the pre-training and adding a “note-taking” component to the masked language model which augments these words with an extra “note” embedding at the in... | SP:e6d36114cad7f28f80e91de6c3536cd6a93d962a |
Taking Notes on the Fly Helps Language Pre-Training | 1 INTRODUCTION . Unsupervised language pre-training , e.g. , BERT ( Devlin et al. , 2018 ) , is shown to be a successful way to improve the performance of various NLP downstream tasks . However , as the pre-training task requires no human labeling effort , a massive scale of training corpus from the Web can be used to ... | The paper proposes an external memory architecture. When encountering the rare words (with a frequency between 100-500), the method will store the average contextualized word embedding of nearby words into a dictionary. Next time it encounters the same rare word, it will retrieve the average embedding and input it into... | SP:e6d36114cad7f28f80e91de6c3536cd6a93d962a |
Taking Notes on the Fly Helps Language Pre-Training | 1 INTRODUCTION . Unsupervised language pre-training , e.g. , BERT ( Devlin et al. , 2018 ) , is shown to be a successful way to improve the performance of various NLP downstream tasks . However , as the pre-training task requires no human labeling effort , a massive scale of training corpus from the Web can be used to ... | This work aims at accelerating pre-training by leveraging the contextual embeddings for the rare words. It is argued that the inadequate training of rare words slows down the pre-training. The authors then proposed to keep a moving average of the contextual embeddings for the rare words and use it to augment the input ... | SP:e6d36114cad7f28f80e91de6c3536cd6a93d962a |
Finding Patient Zero: Learning Contagion Source with Graph Neural Networks | 1 INTRODUCTION . The ability to quickly identify the origin of an outbreak , or “ finding patient zero ” , is critically important in the effort to contain an emerging epidemic . The identification of early transmission chains and the reconstruction of the possible paths of diffusion of the virus can be the difference ... | This paper studies the problem of source detection in an epidemics when one observes the underlying graph and a snapshot of the population at a given time i.e. who is infected or not infected. For a SIR (or SEIR) model, the authors propose to use GNN for this task. The learning procedure is then the following: given a ... | SP:27753b9dcce0cb9b4d764b5721f0b5a20496ff68 |
Finding Patient Zero: Learning Contagion Source with Graph Neural Networks | 1 INTRODUCTION . The ability to quickly identify the origin of an outbreak , or “ finding patient zero ” , is critically important in the effort to contain an emerging epidemic . The identification of early transmission chains and the reconstruction of the possible paths of diffusion of the virus can be the difference ... | Backtracking source of an epidemic (Patient Zero (P0)) is one of the important research topics of the current era that helps efficient resource allocation. Many of the existing works in this domain use graph-theoretic measures or message passing algorithms to tackle this problem. In contrast, this paper uses recently e... | SP:27753b9dcce0cb9b4d764b5721f0b5a20496ff68 |
Finding Patient Zero: Learning Contagion Source with Graph Neural Networks | 1 INTRODUCTION . The ability to quickly identify the origin of an outbreak , or “ finding patient zero ” , is critically important in the effort to contain an emerging epidemic . The identification of early transmission chains and the reconstruction of the possible paths of diffusion of the virus can be the difference ... | An S(E)IR epidemics propagates on a graph, and the goal is to detect its source (P0) only from the observation of the state (S,E,I,R) of every node of the graph at some time $T > 0$. This version of the source detection problem has been studied first by Shah and Zeman (2011) for SI epidemics, as listed in Section 2. Th... | SP:27753b9dcce0cb9b4d764b5721f0b5a20496ff68 |
Iterative Amortized Policy Optimization | 1 Introduction . Reinforcement learning ( RL ) algorithms involve policy evaluation and policy optimization [ 73 ] . Given a policy , one can estimate the value for each state or state-action pair following that policy , and given a value estimate , one can improve the policy to maximize the value . This latter procedu... | The paper proposes to replace the actor/policy network with an iterative version to encode the action distribution parameters, which is inspired by prior work on iterative amortized optimization. This scheme generates the action distribution parameters for each state at the end of an inner loop which takes the objectiv... | SP:cf5222ed98e5d552771fb915beeee4d504b947fe |
Iterative Amortized Policy Optimization | 1 Introduction . Reinforcement learning ( RL ) algorithms involve policy evaluation and policy optimization [ 73 ] . Given a policy , one can estimate the value for each state or state-action pair following that policy , and given a value estimate , one can improve the policy to maximize the value . This latter procedu... | **Contributions**: The authors propose to use iterative amortization for policy optimization to help reduce suboptimality in policy optimization. They find that like in variational inference for generative modeling, iterative amortization is able cover multiple modes of distributions and lower the amortization gap, and... | SP:cf5222ed98e5d552771fb915beeee4d504b947fe |
Iterative Amortized Policy Optimization | 1 Introduction . Reinforcement learning ( RL ) algorithms involve policy evaluation and policy optimization [ 73 ] . Given a policy , one can estimate the value for each state or state-action pair following that policy , and given a value estimate , one can improve the policy to maximize the value . This latter procedu... | This paper draws an interesting connection to variational inference, categorizing current policy optimization methods with KL regularization as direct amortized optimizers. The paper shows how direct amortized policy optimization can be suboptimal and proposes a new class of method called iterative amortized policy opt... | SP:cf5222ed98e5d552771fb915beeee4d504b947fe |
Dream and Search to Control: Latent Space Planning for Continuous Control | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has been effective at solving sequential decision-making problems with varying levels of difficulty . The solutions generally fall into one of two categories : model-free and model-based methods . Model-free methods ( Haarnoja et al. , 2018 ; Silver et al. , 2014 ; Li... | This paper extends Dreamer, a model-based RL algorithm trained through latent imagination, by additionally performing decision-time planning in the learned latent-space dynamics for action selection. Most of the components follow those of Dreamer: from the experiences collected by the agent, it learns a (latent-space) ... | SP:b29c4295d1c4d89280ef2a44d75b5f96e53e70b7 |
Dream and Search to Control: Latent Space Planning for Continuous Control | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has been effective at solving sequential decision-making problems with varying levels of difficulty . The solutions generally fall into one of two categories : model-free and model-based methods . Model-free methods ( Haarnoja et al. , 2018 ; Silver et al. , 2014 ; Li... | The paper is developed on top of the Dreamer architecture, i.e. learning a latent space dynamics based on the image inputs to train policies. The difference is that, instead of using the already trained policy, this paper used MPC or MCTS to sample actions during the exploration phase to reduce the bias. The authors d... | SP:b29c4295d1c4d89280ef2a44d75b5f96e53e70b7 |
Dream and Search to Control: Latent Space Planning for Continuous Control | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has been effective at solving sequential decision-making problems with varying levels of difficulty . The solutions generally fall into one of two categories : model-free and model-based methods . Model-free methods ( Haarnoja et al. , 2018 ; Silver et al. , 2014 ; Li... | This paper proposes to integrate planning into Dreamer. The main idea is to apply a planning module on top of Dreamer to improve the quality of action selection. The planning via MCTS is on the learnt latent dynamics and the policy learnt by Dreamer. One of the challenges addressed in the paper is to perform planning o... | SP:b29c4295d1c4d89280ef2a44d75b5f96e53e70b7 |
Safety Aware Reinforcement Learning (SARL) | 1 INTRODUCTION . Reinforcement learning ( RL ) algorithms have seen great research advances in recent years , both in theory and in their applications to concrete engineering problems . The application of RL algorithms extends to computer games ( Mnih et al. , 2013 ; Silver et al. , 2017 ) , robotics ( Gu et al. , 2017... | This paper proposes a safety-aware reinforcement learning algorithm that learns to perform tasks with minimal side-effects. The key idea is that a safety policy is learned independent of the task reward. When learning the task, this safety policy is incorporated by minimizing the distance between the task agent and the... | SP:dbb93ea184c9798f5a7ffeeef8fbadf3ebd3eac5 |
Safety Aware Reinforcement Learning (SARL) | 1 INTRODUCTION . Reinforcement learning ( RL ) algorithms have seen great research advances in recent years , both in theory and in their applications to concrete engineering problems . The application of RL algorithms extends to computer games ( Mnih et al. , 2013 ; Silver et al. , 2017 ) , robotics ( Gu et al. , 2017... | The paper aims to reduce the unwanted side effects of the actions of a reward-maximizing reinforcement-learning (RL) agent. The authors study a framework in which the environment issues a metric that measures the total side effects of the agent's actions at the end of each episode. The work's proposed solution trains a... | SP:dbb93ea184c9798f5a7ffeeef8fbadf3ebd3eac5 |
Safety Aware Reinforcement Learning (SARL) | 1 INTRODUCTION . Reinforcement learning ( RL ) algorithms have seen great research advances in recent years , both in theory and in their applications to concrete engineering problems . The application of RL algorithms extends to computer games ( Mnih et al. , 2013 ; Silver et al. , 2017 ) , robotics ( Gu et al. , 2017... | This paper aims to address the issue of mitigating side effects in policy learning. The authors propose an algorithm SARL, which uses a safe policy to define a regularization term for penalizing the agent's actions deviating from the safe agent in policy learning. In the experiments, four variations for SARL are shown ... | SP:dbb93ea184c9798f5a7ffeeef8fbadf3ebd3eac5 |
ResNet After All: Neural ODEs and Their Numerical Solution | 1 INTRODUCTION . The choice of neural network architecture is an important consideration in the deep learning community . Among a plethora of options , Residual Neural Networks ( ResNets ) ( He et al. , 2016 ) have emerged as an important subclass of models , as they mitigate the gradient issues ( Balduzzi et al. , 201... | The authors show that Neural ODEs exploit the ODE-solver used for training to realize a dynamical system that violates the ODE vector field property of non-overlapping trajectories. The authors conclude that NODEs are not real ODEs, hence the paper's title "ResNet after all.". To avoid such behavior, the authors propos... | SP:5404aa9f7dfb70080947d9629768cb39a25b84ed |
ResNet After All: Neural ODEs and Their Numerical Solution | 1 INTRODUCTION . The choice of neural network architecture is an important consideration in the deep learning community . Among a plethora of options , Residual Neural Networks ( ResNets ) ( He et al. , 2016 ) have emerged as an important subclass of models , as they mitigate the gradient issues ( Balduzzi et al. , 201... | This paper empirically studies whether Neural ODEs have a valid ODE interpretation. The authors show that a Neural ODE model does not necessarily represent a continuous dynamical system if the discretization of the numerical method is too coarse. Indeed, this is a widely overlooked issue that has been largely ignored i... | SP:5404aa9f7dfb70080947d9629768cb39a25b84ed |
ResNet After All: Neural ODEs and Their Numerical Solution | 1 INTRODUCTION . The choice of neural network architecture is an important consideration in the deep learning community . Among a plethora of options , Residual Neural Networks ( ResNets ) ( He et al. , 2016 ) have emerged as an important subclass of models , as they mitigate the gradient issues ( Balduzzi et al. , 201... | The paper demonstrates how neural ODE models generating features for downstream tasks (or simply modelling trajectories) may rely on the discreteness of integration methods to generate features and thus fail in the exact ODE limit of integration step-size going to zero. The paper highlights particular failure modes, su... | SP:5404aa9f7dfb70080947d9629768cb39a25b84ed |
Self-supervised Visual Reinforcement Learning with Object-centric Representations | 1 INTRODUCTION . Reinforcement learning ( RL ) includes a promising class of algorithms that have shown capability to solve challenging tasks when those tasks are well specified by suitable reward functions . However , in the real world , people are rarely given a well-defined reward function . Indeed , humans are exce... | The paper combines an existing generative world model (SCALOR, Jiang et al. 2019) with goal-conditioned attention policy. The method is evaluated on object manipulation environments based on MuJoCo (Todorov et al., 2012), Multiworld (Nair et al. 2018) and a Sawyer arm. The paper is clearly written; the authors discuss ... | SP:37b8cd02d6c638f0f2e6687c614ae86c0a7bc250 |
Self-supervised Visual Reinforcement Learning with Object-centric Representations | 1 INTRODUCTION . Reinforcement learning ( RL ) includes a promising class of algorithms that have shown capability to solve challenging tasks when those tasks are well specified by suitable reward functions . However , in the real world , people are rarely given a well-defined reward function . Indeed , humans are exce... | This work proposes to use object-centric unsupervised representation learning for self-supervised goal-conditioned RL, as opposed to prior work that assumes no particular structure on the learned representations (eg. VAEs). The proposed method, self-supervised multi-object RL (SMORL), uses the SCALOR architecture from ... | SP:37b8cd02d6c638f0f2e6687c614ae86c0a7bc250 |
Self-supervised Visual Reinforcement Learning with Object-centric Representations | 1 INTRODUCTION . Reinforcement learning ( RL ) includes a promising class of algorithms that have shown capability to solve challenging tasks when those tasks are well specified by suitable reward functions . However , in the real world , people are rarely given a well-defined reward function . Indeed , humans are exce... | The paper proposes to use object-centric representations for RL, which can efficiently handle multiple objects in the scene. To learn a policy that can take a variable number of object observations, the paper proposes the goal-conditioned attention policy, which can focus on objects of interests to achieve each sub-goa... | SP:37b8cd02d6c638f0f2e6687c614ae86c0a7bc250 |
Simplifying Models with Unlabeled Output Data | 1 INTRODUCTION . We study problems whose outputs have validity constraints . For example , in pseudocode-to-code translation , the output code must compile . Other examples include natural language translation and molecule generation , where outputs should be grammatically correct or chemically valid , respectively . S... | The paper introduces a “predict-and-denoise” model for structured prediction, specifically for tasks where the output has to adhere to some constraints e.g. natural language, code etc. This framework allows leveraging of unlabelled output data to train the denoiser, which consequently allows the base predictor to be of... | SP:c56a2ed56cb664f09aaecb355c0012a57f61d245 |
Simplifying Models with Unlabeled Output Data | 1 INTRODUCTION . We study problems whose outputs have validity constraints . For example , in pseudocode-to-code translation , the output code must compile . Other examples include natural language translation and molecule generation , where outputs should be grammatically correct or chemically valid , respectively . S... | The authors propose a more data-efficient way to train generative models with constraints on the output; specifically they evaluate on image generation and pseudocode-to-code (SPoC) tasks. They train two separate models, a “predictor” and a “denoiser”, which they then compose: the output from the “predictor” is further... | SP:c56a2ed56cb664f09aaecb355c0012a57f61d245 |
Simplifying Models with Unlabeled Output Data | 1 INTRODUCTION . We study problems whose outputs have validity constraints . For example , in pseudocode-to-code translation , the output code must compile . Other examples include natural language translation and molecule generation , where outputs should be grammatically correct or chemically valid , respectively . S... | This paper proposes a framework for problems where the output has some validity constraints, for e.g. the output must be a valid python program that must compile. These kind of problems arise naturally in settings such as pseudocode to program, and moreover there are many more unlabelled valid programs that are easily ... | SP:c56a2ed56cb664f09aaecb355c0012a57f61d245 |
Training GANs with Stronger Augmentations via Contrastive Discriminator | 1 INTRODUCTION . Generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ) have become one of the most prominent approaches for generative modeling with a wide range of applications ( Ho & Ermon , 2016 ; Zhu et al. , 2017 ; Karras et al. , 2019 ; Rott Shaham et al. , 2019 ) . In general , a GAN is defined b... | The authors propose to improve GAN training by incorporating augmentations from contrastive learning. Specifically, a new contrastive discriminator, named ContraD, is proposed for GANs; with ContraD, the encoder part of the discriminator is trained with (two) contrastive learning losses, while the left discriminator he... | SP:a7f1259c98d2b58e608bf64299e2790351f78099 |
Training GANs with Stronger Augmentations via Contrastive Discriminator | 1 INTRODUCTION . Generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ) have become one of the most prominent approaches for generative modeling with a wide range of applications ( Ho & Ermon , 2016 ; Zhu et al. , 2017 ; Karras et al. , 2019 ; Rott Shaham et al. , 2019 ) . In general , a GAN is defined b... | In this paper, the authors suggest using the contrastive loss to improve the training of the discriminator and further stabilize the GAN training process. More specifically, the proposed method incorporates the self-supervised simCLR contrastive loss on a pair of transformed real images and supervised contrastive loss ... | SP:a7f1259c98d2b58e608bf64299e2790351f78099 |
Training GANs with Stronger Augmentations via Contrastive Discriminator | 1 INTRODUCTION . Generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ) have become one of the most prominent approaches for generative modeling with a wide range of applications ( Ho & Ermon , 2016 ; Zhu et al. , 2017 ; Karras et al. , 2019 ; Rott Shaham et al. , 2019 ) . In general , a GAN is defined b... | This paper improves upon state-of-the-art GANs by incorporating recent advances of contrastive representation learning into the training of discriminator. In particular, the discriminator loss function consists of three terms: (1) the original SimCLR loss on the multi-view real data pairs; (2) the supervised contrastiv... | SP:a7f1259c98d2b58e608bf64299e2790351f78099 |
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks | 1 INTRODUCTION . Graph neural networks ( GNNs ) ( Gori et al. , 2005 ; Scarselli et al. , 2008 ; Bronstein et al. , 2017 ; Battaglia et al. , 2018 ) have become very popular recently due to their ability to learn powerful representations from graph-structured data , and have achieved state-of-the-art results in a varie... | This paper provides results of generalization bounds for two types of GNNs: GCN and MPGNN. The presented analysis follows the framework of Neyshabur 2017 to construct posterior by adding random perturbations so that the PAC-Bayesian technique can be applied. The main contributions are the perturbation analysis for GCN ... | SP:edbfbf6b8fa385217b88beaad54cfa64fd814c58 |
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks | 1 INTRODUCTION . Graph neural networks ( GNNs ) ( Gori et al. , 2005 ; Scarselli et al. , 2008 ; Bronstein et al. , 2017 ; Battaglia et al. , 2018 ) have become very popular recently due to their ability to learn powerful representations from graph-structured data , and have achieved state-of-the-art results in a varie... | In this paper, the authors propose generalization bounds for GNNs, both convolutional and standard message passing variants. The result is a generalization of those for CNN/MLP architectures with relu activation functions. The analysis method closely follows those established in the former settings as well. The specifi... | SP:edbfbf6b8fa385217b88beaad54cfa64fd814c58 |
A PAC-Bayesian Approach to Generalization Bounds for Graph Neural Networks | 1 INTRODUCTION . Graph neural networks ( GNNs ) ( Gori et al. , 2005 ; Scarselli et al. , 2008 ; Bronstein et al. , 2017 ; Battaglia et al. , 2018 ) have become very popular recently due to their ability to learn powerful representations from graph-structured data , and have achieved state-of-the-art results in a varie... | The paper presents PAC-Bayesian generalization bounds for two classes of graph neural networks: graph convolutional neural networks and message passing graph neural networks. The paper essentially adapts Neyshabur et al. (2017) PAC-Bayesian margin bounds for neural networks to graph neural networks and expectedly the b... | SP:edbfbf6b8fa385217b88beaad54cfa64fd814c58 |
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs | Adversarial training has been the topic of dozens of studies and a leading method for defending against adversarial attacks . Yet , it remains largely unknown ( a ) how adversarially-robust ImageNet classifiers ( R classifiers ) generalize to out-ofdistribution examples ; and ( b ) how their generalization capability r... | This paper takes a step further to understand the relationships between the adversarial trained CNNs (R-CNNs) and shape-based representation, and delve deeper into the R-CNNs via studying the hidden units. First, it justifies that the R-CNNs prefer shape cues based on random-shuffled, Stylized-ImageNet, and silhouette ... | SP:d7243cbcc28ef5efc3891d4220342e818c87d188 |
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs | Adversarial training has been the topic of dozens of studies and a leading method for defending against adversarial attacks . Yet , it remains largely unknown ( a ) how adversarially-robust ImageNet classifiers ( R classifiers ) generalize to out-ofdistribution examples ; and ( b ) how their generalization capability r... | In this paper, the authors show that adversarially robust versions of three popular CNN architectures trained for image classification on ImageNet rely on shape rather than on textures to perform recognition. They also show that adversarially robust networks do not outperform non-robust networks on corrupted data. Fina... | SP:d7243cbcc28ef5efc3891d4220342e818c87d188 |
The shape and simplicity biases of adversarially robust ImageNet-trained CNNs | Adversarial training has been the topic of dozens of studies and a leading method for defending against adversarial attacks . Yet , it remains largely unknown ( a ) how adversarially-robust ImageNet classifiers ( R classifiers ) generalize to out-ofdistribution examples ; and ( b ) how their generalization capability r... | The submission concerns an experimental study of the behavior of networks trained with and without adversarial robustness criteria (using Madry et al., 2017). Given a set of such trained networks, a detailed look at the behavior and properties of adversarially robust networks and their non-robust counterparts is taken.... | SP:d7243cbcc28ef5efc3891d4220342e818c87d188 |
No Spurious Local Minima: on the Optimization Landscapes of Wide and Deep Neural Networks | Empirical studies suggest that wide neural networks are comparably easy to optimize , but mathematical support for this observation is scarce . In this paper , we analyze the optimization landscapes of deep learning with wide networks . We prove especially that constraint and unconstraint empirical-risk minimization ov... | This paper studies the optimization landscape of the training loss of deep neural networks. For a general setup, the paper shows that if the network width is greater than $2m(n+1)^l$, then any parameter value has a path to a global minimum on which the training loss does not increase. Here, $m$ is the output dimension... | SP:44d8612c0df236bd137f053aa3d9ea450731f46a |
No Spurious Local Minima: on the Optimization Landscapes of Wide and Deep Neural Networks | Empirical studies suggest that wide neural networks are comparably easy to optimize , but mathematical support for this observation is scarce . In this paper , we analyze the optimization landscapes of deep learning with wide networks . We prove especially that constraint and unconstraint empirical-risk minimization ov... | This work showed that for a wide deep neural network, the optimization landscapes of empirical-risk minimizers over wide feedforward networks have no spurious local minima. The theory combines the features of the two mentioned works, as it applies to the entire optimization landscapes, allows for a wide spectrum of los... | SP:44d8612c0df236bd137f053aa3d9ea450731f46a |
No Spurious Local Minima: on the Optimization Landscapes of Wide and Deep Neural Networks | Empirical studies suggest that wide neural networks are comparably easy to optimize , but mathematical support for this observation is scarce . In this paper , we analyze the optimization landscapes of deep learning with wide networks . We prove especially that constraint and unconstraint empirical-risk minimization ov... | [Summary] This paper considers the optimization landscape of deep learning with very wide networks. The main contribution of this paper is the result that shows the empirical risk of very wide networks has no *spurious local minima*. The results hold for both constrained (like the norm or sparsity constraints on the ne... | SP:44d8612c0df236bd137f053aa3d9ea450731f46a |
Gradient Origin Networks | 1 INTRODUCTION . Observable data in nature has some parameters which are known , such as local coordinates , but also some unknown parameters such as how the data is related to other examples . Generative models , which learn a distribution over observables , are central to our understanding of patterns in nature and a... | The paper proposes GONs which seek to build a generative model with an “implicit” encoder that comes essentially for free with the use of a few re-parameterization tricks. The main idea being that existing generative models with an encoder are “redundant” in that the decoder itself has the ability to compute the gradie... | SP:7a198931c36fb0b660c283036cbe475bb16750c9 |
Gradient Origin Networks | 1 INTRODUCTION . Observable data in nature has some parameters which are known , such as local coordinates , but also some unknown parameters such as how the data is related to other examples . Generative models , which learn a distribution over observables , are central to our understanding of patterns in nature and a... | This paper proposes a new type of generative models with a new inference method of latent variables. Specifically, the gradient of latent variables with respect to zero vector is taken as the inferred latent variables. Based on this, the authors generalize the propose model to implicit and variational versions and demo... | SP:7a198931c36fb0b660c283036cbe475bb16750c9 |
Gradient Origin Networks | 1 INTRODUCTION . Observable data in nature has some parameters which are known , such as local coordinates , but also some unknown parameters such as how the data is related to other examples . Generative models , which learn a distribution over observables , are central to our understanding of patterns in nature and a... | This paper introduces a "new" inference method for autoencoder-type models, where the encoder is taken as a gradient of the decoder with respect to a zero-initialized latent variable. The method is evaluated for both a deterministic autoencoder and a VAE on toy image data (cifar10 being the most complex of them) and ap... | SP:7a198931c36fb0b660c283036cbe475bb16750c9 |
On the Neural Tangent Kernel of Equilibrium Models | 1 INTRODUCTION Recent works empirically observe that as the depth of a weight-tied input-injected network increases , its output tends to converge to a fixed point . Motivated by this phenomenon , DEQ models were proposed to effectively represent an “ infinite depth ” network by root-finding . A natural question to ask... | This paper studies the double infinite-width + infinite-depth limit of fully connected and convolutional neural nets from an NTK angle, when input injections enter the picture. The techniques mix NTK techniques with Deep Equilibrium (DEQ) model techniques to directly compute the infinite-depth limit of the infinite-wid... | SP:3bd9aeb125e33dd92db4510d3c2c26e782576ab8 |
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