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Byzantine-Robust Learning on Heterogeneous Datasets via Resampling | 1 INTRODUCTION . Distributed or federated machine learning , where the data is distributed across multiple workers , has become an increasingly important learning paradigm both due to growing sizes of datasets , as well as privacy and security concerns . In such a setting , the workers collaborate to train a single mod... | Many existing researches on distributed deep learning works in byzantine robustness in a centralized PS setting under the assumption of i.i.d. data distribution on workers, e.g. KRUM. This paper presents a simple resampling scheme that adapts the existing robust algorithms to heterogeneous datasets (referred as KRUM-RS... | SP:72f25cb455c7d0e0a2ce818d3baab4a916376dd3 |
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction | 1 INTRODUCTION . The manufactured objects that surround us in everyday life are created in computer-aided design ( CAD ) software using common modeling operations such as sketch and extrude . With just these two modeling operations , a highly expressive range of 3D designs can be created ( Figure 1 ) . Parametric CAD f... | The paper presents the first large scale CAD construction sequence dataset together with environment that allows us to synthesize 3D CAD from these sequences. The dataset and environment are essential building blocks for applying machine learning algorithms to CAD design process. Furthermore, the paper proposes compreh... | SP:96e936fc1cf2e77c2bf154a66da19aa9a1f68400 |
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction | 1 INTRODUCTION . The manufactured objects that surround us in everyday life are created in computer-aided design ( CAD ) software using common modeling operations such as sketch and extrude . With just these two modeling operations , a highly expressive range of 3D designs can be created ( Figure 1 ) . Parametric CAD f... | The paper describes a new dataset of 3D geometry construction sequences based on sequential sketching (i.e. sets of two-dimensional curves) and extruding (i.e. axes, angles and distances for extrusion profiles from sketches) combined with Boolean solid geometry operations. The dataset comprises the resulting objects as... | SP:96e936fc1cf2e77c2bf154a66da19aa9a1f68400 |
Learning to Solve Nonlinear Partial Differential Equation Systems To Accelerate MOSFET Simulation | 1 INTRODUCTION . Nonlinear partial differential equations ( PDEs ) appear frequently in many science and engineering problems including transport equations for certain quantities like heat , mass , momentum , and energy ( Fischetti & Vandenberghe , 2016 ) . The Maxwell equations for the electromagnetic fields ( Jackson... | The paper proposes to use a CNN to compute an initial guess for the iterative Newton-Rhapson solution of a coupled PDE system used for semiconductor device simulation. To do so, the authors construct a "device template" which parametrizes the design space. The CNN then maps a device configuration in this 6-dim space to... | SP:a5f75e1ad5a0650b7d39d4a0c74e1f20884f3680 |
Learning to Solve Nonlinear Partial Differential Equation Systems To Accelerate MOSFET Simulation | 1 INTRODUCTION . Nonlinear partial differential equations ( PDEs ) appear frequently in many science and engineering problems including transport equations for certain quantities like heat , mass , momentum , and energy ( Fischetti & Vandenberghe , 2016 ) . The Maxwell equations for the electromagnetic fields ( Jackson... | Contribution: Authors propose using a neural network to learn an approximate solution for desired boundary conditions in order to accelerate the semiconductor device simulation. They significately reduce the computational cost to calculate several unnecessary solutions when considering an initial solution sufficiently ... | SP:a5f75e1ad5a0650b7d39d4a0c74e1f20884f3680 |
Open Question Answering over Tables and Text | 1 INTRODUCTION . Open question answering considers the problem of retrieving documents from a fixed corpus with a retriever , and then analyzes retrieved evidence to provide answers to a given question with a reader . Prior open question answering systems focused only on retrieving and reading free-form passages or doc... | In this paper, the authors introduce the task of open domain QA using table and text. Unlike recent tasks on table and text retrieval, where the table associated with the text is known, in this task both tables and text need to be retrieved from a large collection. Similar to the recent HybridQA task, questions may req... | SP:f4a2e63fc2cd7787434a51f3a9511ed64b6a7e0d |
Open Question Answering over Tables and Text | 1 INTRODUCTION . Open question answering considers the problem of retrieving documents from a fixed corpus with a retriever , and then analyzes retrieved evidence to provide answers to a given question with a reader . Prior open question answering systems focused only on retrieving and reading free-form passages or doc... | The paper provides a interesting direction in open question answering. In particular, it proposes an open QA problem over both tabular and textual data, and present a new large-scale dataset Open Table-and-Text Question Answering (OTT-QA) to evaluate performance on this task. Two techniques are introduced to address th... | SP:f4a2e63fc2cd7787434a51f3a9511ed64b6a7e0d |
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization | 1 INTRODUCTION . Federated learning ( FL ) , has gained popularity for various applications involving learning from distributed data . In FL , a cloud server ( the “ server ” ) can communicate with distributed data sources ( the “ clients ” ) , while the clients hold data separately . A major challenge in FL is the tra... | This work proposes an extremely simple approach to a well-know problem within Federated Learning: batch normalisation. Indeed, FL usually imply an averaging of different model parameters trained on different distributed devices. But what happen with the running statistics that some training methods have ? Such as batch... | SP:3fc909ff04f888517dc1b7fdfad3ca019ead54b5 |
FedBN: Federated Learning on Non-IID Features via Local Batch Normalization | 1 INTRODUCTION . Federated learning ( FL ) , has gained popularity for various applications involving learning from distributed data . In FL , a cloud server ( the “ server ” ) can communicate with distributed data sources ( the “ clients ” ) , while the clients hold data separately . A major challenge in FL is the tra... | This paper develops a modified version of FedAvg by local batch normalization that is tailored for federated learning with non-i.i.d. data. Different from most of existing work that consider the unbalanced labels, this paper uses unbalanced features to motivate the non-i.i.d. federated settings. Specifically, the unbal... | SP:3fc909ff04f888517dc1b7fdfad3ca019ead54b5 |
Towards Robustness against Unsuspicious Adversarial Examples | Despite the remarkable success of deep neural networks , significant concerns have emerged about their robustness to adversarial perturbations to inputs . While most attacks aim to ensure that these are imperceptible , physical perturbation attacks typically aim for being unsuspicious , even if perceptible . However , ... | I like the idea to check whether it might be possible that noise can be injected selectively in areas where it is likely to make the adversarial example less suspicious and thus it might be possible to inject more noise in those directions. But the challenge is that this might increase the saliency of those areas. The ... | SP:40e6f3c9b45c37a5580314fde5407e7ceab19fa9 |
Towards Robustness against Unsuspicious Adversarial Examples | Despite the remarkable success of deep neural networks , significant concerns have emerged about their robustness to adversarial perturbations to inputs . While most attacks aim to ensure that these are imperceptible , physical perturbation attacks typically aim for being unsuspicious , even if perceptible . However , ... | The paper studies threat models for images which take into account the foreground and background within the image. Specifically, they use DeepGaze II to identify pixels as either foreground or background, and use an Lp threat model with a larger radius in the background and a smaller radius in the foreground. The resul... | SP:40e6f3c9b45c37a5580314fde5407e7ceab19fa9 |
Probabilistic Numeric Convolutional Neural Networks | 1 INTRODUCTION . Standard convolutional neural networks are defined on a regular input grid . For continuous signals like time series and images , these elements correspond to regular samples of an underlying function f defined on a continuous domain . In this case , the standard convolutional layer of a neural network... | This work presents an uncertainty aware continuous convolutional layers for learning from continuous signals like time series/images. This work is most useful in the setting of irregularly sampled data. Gaussian processes (GP) are used to represent the irregularly sampled input. The proposed continuous convolutional la... | SP:70ffdb2504b0c72a7be46dec6a519b9717fb7d41 |
Probabilistic Numeric Convolutional Neural Networks | 1 INTRODUCTION . Standard convolutional neural networks are defined on a regular input grid . For continuous signals like time series and images , these elements correspond to regular samples of an underlying function f defined on a continuous domain . In this case , the standard convolutional layer of a neural network... | The paper considers the problem of using CNNs on an irregularly sampled grid. It proposes to incorporate the numerical uncertainty related to the disretisation of the domain using an approach inspired by probabilistic numerics. This involves defining continuous convolutional layers as affine transformations of the inpu... | SP:70ffdb2504b0c72a7be46dec6a519b9717fb7d41 |
Uncertainty Estimation in Autoregressive Structured Prediction | 1 INTRODUCTION . Neural Networks ( NNs ) have become the dominant approach in numerous applications ( Simonyan & Zisserman , 2015 ; Mikolov et al. , 2013 ; 2010 ; Bahdanau et al. , 2015 ; Vaswani et al. , 2017 ; Hinton et al. , 2012 ) and are being widely deployed in production . As a consequence , predictive uncertain... | This paper proposes two different measures of knowledge (epistemic) uncertainty in structured prediction with an autoregressive model and discusses how to compute their approximations. The main contribution is the proposed reverse mutual information (RMI) as a measure of epistemic uncertainty in structured prediction. ... | SP:c73fc3212d75833f5f54e4d76326b759fa9442c4 |
Uncertainty Estimation in Autoregressive Structured Prediction | 1 INTRODUCTION . Neural Networks ( NNs ) have become the dominant approach in numerous applications ( Simonyan & Zisserman , 2015 ; Mikolov et al. , 2013 ; 2010 ; Bahdanau et al. , 2015 ; Vaswani et al. , 2017 ; Hinton et al. , 2012 ) and are being widely deployed in production . As a consequence , predictive uncertain... | This work introduce rigorous information-theoretic measures for structured prediction tasks. It proposed metrics for both `"total uncertainty" (entropy) and "knowledge uncertainty" (MI, EPKL and RMI) on the sequence level, introduced efficient Monte-Carlo approaches to estimate them in practice. Finally, author conduct... | SP:c73fc3212d75833f5f54e4d76326b759fa9442c4 |
Pre-Training by Completing Point Clouds | 1 INTRODUCTION . Point clouds are a natural representation of 3D objects . Recently , there has been a flurry of exciting new point cloud models in areas such as segmentation ( Landrieu & Simonovsky , 2018 ; Yang et al. , 2019a ; Hu et al. , 2020a ) and object detection ( Zhou & Tuzel , 2018 ; Lang et al. , 2019 ; Wang... | This paper proposes a better pre-trained prior for a variety of downstream applications in point cloud analysis. The workflow of the pre-training mechanism is to first 1) generate occluded points that result from view occlusion and then 2) optimize the encoder to learn how to complete the occluded points from the parti... | SP:4673d78e21f98a766c4340809623e54d165f69d9 |
Pre-Training by Completing Point Clouds | 1 INTRODUCTION . Point clouds are a natural representation of 3D objects . Recently , there has been a flurry of exciting new point cloud models in areas such as segmentation ( Landrieu & Simonovsky , 2018 ; Yang et al. , 2019a ; Hu et al. , 2020a ) and object detection ( Zhou & Tuzel , 2018 ; Lang et al. , 2019 ; Wang... | The paper considers the problem of training networks for point cloud processing through a point cloud completion task. Given a point cloud, it is rendered from a set of viewpoints and for each viewpoint the set of visible points is determine. A network is then trained to generate the full point cloud from the partially... | SP:4673d78e21f98a766c4340809623e54d165f69d9 |
Quantum Deformed Neural Networks | We develop a new quantum neural network layer designed to run efficiently on a quantum computer but that can be simulated on a classical computer when restricted in the way it entangles input states . We first ask how a classical neural network architecture , both fully connected or convolutional , can be executed on a... | The authors introduced the idea of quantum deformed neural network (DQNN) which allows substitution of the positive probabilities in Baysian statistics with complex amplitude inspired by Born representation of quantum wavefunction, thus allowing interference phenomena leading to possible speed up for running neural net... | SP:efef763fd83336660ba89b2070325cca5b989fe4 |
Quantum Deformed Neural Networks | We develop a new quantum neural network layer designed to run efficiently on a quantum computer but that can be simulated on a classical computer when restricted in the way it entangles input states . We first ask how a classical neural network architecture , both fully connected or convolutional , can be executed on a... | The fundamental idea in this paper is to endow classical signals with a complex Hilberspace structure so as to harness the probabilistic nature of quantum mechaniscs for pattern recognition based on quantum computing principles. Following this idea, the authors consider (probabiistic) binary neural networks and develop... | SP:efef763fd83336660ba89b2070325cca5b989fe4 |
Attention-driven Robotic Manipulation | 1 INTRODUCTION . Despite their potential , continuous-control reinforcement learning ( RL ) algorithms have many flaws : they are notoriously data hungry , often fail with sparse rewards , and struggle with long-horizon tasks . The algorithms for both discrete and continuous RL are almost always evaluated on benchmarks... | This work focuses on sparse-reward robotic manipulation tasks from image and point cloud inputs, given a few demonstrations, and proposes an algorithm that consists of a Q-attention module and a confidence-aware critic. The Q-attention module is an RL agent, which takes image and point cloud inputs with pixel positions... | SP:ef1d9ed2d961186f3bcc7bcb6f58cdd9fa57ecca |
Attention-driven Robotic Manipulation | 1 INTRODUCTION . Despite their potential , continuous-control reinforcement learning ( RL ) algorithms have many flaws : they are notoriously data hungry , often fail with sparse rewards , and struggle with long-horizon tasks . The algorithms for both discrete and continuous RL are almost always evaluated on benchmarks... | Motivated by the fact that RL algorithms are notoriously sample inefficient from pixels and that humans use attention, the authors propose Attention-driven Robotic Manipulation (ARM), which they claim can be applied to several robotics tasks without prior task knowledge. Compared to current methods which fail to train,... | SP:ef1d9ed2d961186f3bcc7bcb6f58cdd9fa57ecca |
Clearing the Path for Truly Semantic Representation Learning | 1 INTRODUCTION . The task of unsupervised learning of interpretable data representations has a long history . From classical approaches using linear algebra e.g . via Principle Component Analysis ( PCA ) ( Pearson , 1901 ) or statistical methods such as Independent Component Analysis ( ICA ) ( Comon , 1994 ) all the wa... | This work aims to demonstrate that VAE-based architectures can take advantage of inherent correlation between data to produce representations. They claim that small perturbations of the data prevent these architectures from taking advantage of such correlations resulting in failure to produce disentangled representatio... | SP:02105a375c2a75e53b103af0a9f58dbd2df943e5 |
Clearing the Path for Truly Semantic Representation Learning | 1 INTRODUCTION . The task of unsupervised learning of interpretable data representations has a long history . From classical approaches using linear algebra e.g . via Principle Component Analysis ( PCA ) ( Pearson , 1901 ) or statistical methods such as Independent Component Analysis ( ICA ) ( Comon , 1994 ) all the wa... | This paper shows a new method to hurt the disentanglement of Variational AutoEncoders (VAEs) by only slightly modifying the images. It conducts experiments and shows that the disentanglement metrics drop significantly by this carefully designed perturbation compared to uniformly noise, and the results are consistent ac... | SP:02105a375c2a75e53b103af0a9f58dbd2df943e5 |
Clearing the Path for Truly Semantic Representation Learning | 1 INTRODUCTION . The task of unsupervised learning of interpretable data representations has a long history . From classical approaches using linear algebra e.g . via Principle Component Analysis ( PCA ) ( Pearson , 1901 ) or statistical methods such as Independent Component Analysis ( ICA ) ( Comon , 1994 ) all the wa... | The authors first describe the shortcomings of existing work for disentanged representation learning and the general problem of unidentifiability. Here however, the presentation could be more precise. For example, they state that the log-likelihood objective (2) and the evidence lower bound (3) would be "invariant unde... | SP:02105a375c2a75e53b103af0a9f58dbd2df943e5 |
Evaluating Agents Without Rewards | 1 INTRODUCTION Metric Reward Correlation Task Reward 1.00 Human Similarity 0.67 Input Entropy 0.54 Information Gain 0.49 Empowerment 0.41 Metric Human Correlation Human Similarity 1.00 Input Entropy 0.89 Information Gain 0.79 Task Reward 0.67 Empowerment 0.66 Deep reinforcement learning ( RL ) has enabled agents to sol... | This work studies four task-agnostic metrics for evaluating reinforcement learning agents: human similarity, curiosity, empowerment and information gain. Experiments were conducted with three selected RL algorithms (PPO, ICM and RND) on selected atari games. The results show that a combination of task reward and curios... | SP:95a11b3ebbaf6f3d4ba0f255d7ebd8ef26e48ced |
Evaluating Agents Without Rewards | 1 INTRODUCTION Metric Reward Correlation Task Reward 1.00 Human Similarity 0.67 Input Entropy 0.54 Information Gain 0.49 Empowerment 0.41 Metric Human Correlation Human Similarity 1.00 Input Entropy 0.89 Information Gain 0.79 Task Reward 0.67 Empowerment 0.66 Deep reinforcement learning ( RL ) has enabled agents to sol... | This paper proposes to study three types of intrinsic motivations: curiosity, empowerment and information gain. They propose to compute these measures on the lifetime experience of RL agents and to use them as behavioral metrics. To evaluate these metrics, they perform a correlation study with respect to two traditiona... | SP:95a11b3ebbaf6f3d4ba0f255d7ebd8ef26e48ced |
Evaluating Agents Without Rewards | 1 INTRODUCTION Metric Reward Correlation Task Reward 1.00 Human Similarity 0.67 Input Entropy 0.54 Information Gain 0.49 Empowerment 0.41 Metric Human Correlation Human Similarity 1.00 Input Entropy 0.89 Information Gain 0.79 Task Reward 0.67 Empowerment 0.66 Deep reinforcement learning ( RL ) has enabled agents to sol... | The goal of this paper is to improve our understanding of reward-agnostic metrics drawn from the literature through comparison with human behaviour and task reward. This paper compares two intrinsic reward methods against three baselines on three Atari environments on five metrics, including task reward, a simple metri... | SP:95a11b3ebbaf6f3d4ba0f255d7ebd8ef26e48ced |
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling | 1 INTRODUCTION . “ Ok Google . Hey Siri . Hi Alexa. ” have featured a massive boom of smart speakers in recent years , unveiling a trend towards ubiquitous and ambient Artificial Intelligence ( AI ) for better daily lives . As the communication bridge between human and machine , low-latency streaming ASR ( a.k.a. , onl... | This paper proposes a unified single neural network architecture to realize both streaming and full-context ASR systems. The idea is simple but very efficient. It uses the same model for both streaming and full-context ASR systems, but when we use the streaming mode, some of the network operations that use the future c... | SP:c1096552a3d3382c17df2bb6a2cde86df3d1eb30 |
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling | 1 INTRODUCTION . “ Ok Google . Hey Siri . Hi Alexa. ” have featured a massive boom of smart speakers in recent years , unveiling a trend towards ubiquitous and ambient Artificial Intelligence ( AI ) for better daily lives . As the communication bridge between human and machine , low-latency streaming ASR ( a.k.a. , onl... | This paper proposes an unified framework for both streaming and non-streaming ASR and the knowledge transfer between them. The results show that both latency and performance are improved. The benefit of training full-context and streaming together are two folds: 1) Current full-context and streaming ASR are trained sep... | SP:c1096552a3d3382c17df2bb6a2cde86df3d1eb30 |
Dual-mode ASR: Unify and Improve Streaming ASR with Full-context Modeling | 1 INTRODUCTION . “ Ok Google . Hey Siri . Hi Alexa. ” have featured a massive boom of smart speakers in recent years , unveiling a trend towards ubiquitous and ambient Artificial Intelligence ( AI ) for better daily lives . As the communication bridge between human and machine , low-latency streaming ASR ( a.k.a. , onl... | The paper proposes a pragmatic approach to unifying end-to-end speech-recognition models for whole-utterance and streaming models. Streaming models are defined as not using any future audio, while whole-utterance (or "full context") models can look at the entire audio recording. In short, the paper shows that multi-tas... | SP:c1096552a3d3382c17df2bb6a2cde86df3d1eb30 |
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) have in recent years been shown to provide a scalable and highly performant means of incorporating linguistic information and other structural biases into NLP models . They have been applied to various kinds of representations ( e.g. , syntactic and semantic graphs , co-r... | of the paper*: This paper introduces a post-hoc method -- GraphMask, to interpret the prediction of GNNs. For each edge in the GNNs, the authors introduce a learnable hard gate which indicates whether this edge could be erased or not. The gates can be trained together with the model in a fully differentiable way. By an... | SP:758ab3741de56ebbac4d505a0c15670c55177023 |
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) have in recent years been shown to provide a scalable and highly performant means of incorporating linguistic information and other structural biases into NLP models . They have been applied to various kinds of representations ( e.g. , syntactic and semantic graphs , co-r... | This paper proposes an interpretation method for graph neural networks (GNNs) by learning parameterized, differentiable edge masks. The proposed method uses a single-layer NN classifier to predict whether an edge can be dropped. It further uses L0 norm to encourage sparsity and amortizes parameter learning over a trai... | SP:758ab3741de56ebbac4d505a0c15670c55177023 |
Interpreting Graph Neural Networks for NLP With Differentiable Edge Masking | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) have in recent years been shown to provide a scalable and highly performant means of incorporating linguistic information and other structural biases into NLP models . They have been applied to various kinds of representations ( e.g. , syntactic and semantic graphs , co-r... | This paper presents a new interpreting algorithm for understanding graph neural network (GNN) models for natural language processing (NLP) tasks. The main idea is to remove redundant edges for each layer of GNN after training the model. To find the redundant edges, the authors suggest minimizing the difference between ... | SP:758ab3741de56ebbac4d505a0c15670c55177023 |
Improving Hierarchical Adversarial Robustness of Deep Neural Networks | 1 INTRODUCTION . Deep neural networks ( DNNs ) are highly vulnerable to attacks based on small modification of the input to the network at test time ( Szegedy et al. , 2013 ) . Those adversarial perturbations are carefully crafted in a way that they are imperceptible to human observers , but when added to clean images ... | The authors discuss a new notion of adversarial robustness, specifically, robustness to hierarchical adversarial examples. This is motivated by the idea that some types of misclassifications (e.g. mistaking one type of dog for another) may be less harmful than others (e.g. mistaking a dog for a truck); thus, adversaria... | SP:5252a47e41708a6ebf8defd2333928ef761027d4 |
Improving Hierarchical Adversarial Robustness of Deep Neural Networks | 1 INTRODUCTION . Deep neural networks ( DNNs ) are highly vulnerable to attacks based on small modification of the input to the network at test time ( Szegedy et al. , 2013 ) . Those adversarial perturbations are carefully crafted in a way that they are imperceptible to human observers , but when added to clean images ... | This paper tackles the problem of building hierarchical adversarially robust (HAR) models---i.e., models that are less prone to coarse-grained misclassifications in the face of adversarial manipulation. Specifically, the authors propose HAR networks, wherein the learning/inference problem is decomposed into coarse-grai... | SP:5252a47e41708a6ebf8defd2333928ef761027d4 |
Improving Hierarchical Adversarial Robustness of Deep Neural Networks | 1 INTRODUCTION . Deep neural networks ( DNNs ) are highly vulnerable to attacks based on small modification of the input to the network at test time ( Szegedy et al. , 2013 ) . Those adversarial perturbations are carefully crafted in a way that they are imperceptible to human observers , but when added to clean images ... | This paper investigates adversarial attacks in the hierarchy of labels. Misclassifying a person for a car is a “coarse misclassification”, but misclassifying a bus for a car is a “fine misclassification”. The paper introduces both a metric for hierarchical adversarial robustness, and a method to improve this metric (wh... | SP:5252a47e41708a6ebf8defd2333928ef761027d4 |
Robustness against Relational Adversary | Test-time adversarial attacks have posed serious challenges to the robustness of machine-learning models , and in many settings the adversarial perturbation need not be bounded by small �p-norms . Motivated by the semantics-preserving attacks in vision and security domain , we investigate relational adversaries , a bro... | The paper proposes to define robustness against adversarial attacks using a binary relation over inputs. The binary relation R states which input examples should be classified the same way and each connected component defined by R needs to be assigned the same label leading to robust classification. The idea of using a... | SP:ac40b4df07128cb87ba180c80a4c7d70511bb430 |
Robustness against Relational Adversary | Test-time adversarial attacks have posed serious challenges to the robustness of machine-learning models , and in many settings the adversarial perturbation need not be bounded by small �p-norms . Motivated by the semantics-preserving attacks in vision and security domain , we investigate relational adversaries , a bro... | This paper proposed a new framework, relational adversary, to study adversarial robustness under multiple (sequential) data transformations. The authors also made robustness guarantees based on the Normalizle-and-Predict scenario. Improved robustness is numerically presented on two tasks: malware detection (under add/s... | SP:ac40b4df07128cb87ba180c80a4c7d70511bb430 |
Robustness against Relational Adversary | Test-time adversarial attacks have posed serious challenges to the robustness of machine-learning models , and in many settings the adversarial perturbation need not be bounded by small �p-norms . Motivated by the semantics-preserving attacks in vision and security domain , we investigate relational adversaries , a bro... | This work investigates relational adversary which creates adversarial examples by transforming the original inputs via a logical relation. The authors analyze the conditions for robustness to relational adversary and propose a learning framework which learns and predicts over normalized training and test inputs. Since ... | SP:ac40b4df07128cb87ba180c80a4c7d70511bb430 |
Using Synthetic Data to Improve the Long-range Forecasting of Time Series Data | 1 INTRODUCTION . Short-range forecasting of time series data has been able to provide some useful information , but its scope of application is limited ( Chatfield , 2000 ; Granger & Newbold , 2014 ) . In most applications , long-range forecasting of time series data is preferred as it allows more time for early interv... | This paper proposes cWGAN-GEP for long-range forecasting of time-series data. cWGAN-GEP is a combination of data generation of data prediction, where given some observations, GAN iteratively generates a short synthetic time-series data, and an LSTM subsequently makes a long-range prediction based on the generated synth... | SP:c0695a7e6fe908a51b38f52594678c6d87c8014a |
Using Synthetic Data to Improve the Long-range Forecasting of Time Series Data | 1 INTRODUCTION . Short-range forecasting of time series data has been able to provide some useful information , but its scope of application is limited ( Chatfield , 2000 ; Granger & Newbold , 2014 ) . In most applications , long-range forecasting of time series data is preferred as it allows more time for early interv... | This paper presents a framework for long-range forecasting of time series data using synthetic data. The whole pipeline contains three main components: cWGAN-GEP to predict/generate the following data based on observed ones; an LSTM predictor to forecast results based on real/generated data; and ITC to cluster data poi... | SP:c0695a7e6fe908a51b38f52594678c6d87c8014a |
Using Synthetic Data to Improve the Long-range Forecasting of Time Series Data | 1 INTRODUCTION . Short-range forecasting of time series data has been able to provide some useful information , but its scope of application is limited ( Chatfield , 2000 ; Granger & Newbold , 2014 ) . In most applications , long-range forecasting of time series data is preferred as it allows more time for early interv... | This paper proposes a technique for long range time-series forecasting that leverages a generative model to extend the existing time series to train a time-series model to make the final prediction at horizon N. The authors design a GAN, exploring an enhanced loss function, and provide a methodology by which to train ... | SP:c0695a7e6fe908a51b38f52594678c6d87c8014a |
An Efficient Protocol for Distributed Column Subset Selection in the Entrywise $\ell_p$ Norm | 1 p− 1 2 poly ( log nd ) -approximation to the best possible column subset . A key ingredient in our proof is the reduction to the ` p,2-norm , which corresponds to the p-norm of the vector of Euclidean norms of each of the columns of A . This enables us to use strong coreset constructions for Euclidean norms , which p... | The paper considers the column subset selection (CSS) problem, which has received considerable attention in numerical linear algebra. It considers a distributed variant of CSS in the $\ell_p$ norm, where $p \in [1,2)$. Despite the attention this problem has received previously, it seems like this is a new setting that ... | SP:7843f034ba6da2773202b448221de0886c51f545 |
An Efficient Protocol for Distributed Column Subset Selection in the Entrywise $\ell_p$ Norm | 1 p− 1 2 poly ( log nd ) -approximation to the best possible column subset . A key ingredient in our proof is the reduction to the ` p,2-norm , which corresponds to the p-norm of the vector of Euclidean norms of each of the columns of A . This enables us to use strong coreset constructions for Euclidean norms , which p... | This paper focused $\ell_p$-norm error analysis of distributed column subset selection problems. The proposed distributed algorithm guarantee an $\tilde O(k^{1/p-1/2} )$-approximation to the best subset of columns with $\tilde O(sdk)$ communication cost per round and polynomial time. The lower bound analysis show that... | SP:7843f034ba6da2773202b448221de0886c51f545 |
An Efficient Protocol for Distributed Column Subset Selection in the Entrywise $\ell_p$ Norm | 1 p− 1 2 poly ( log nd ) -approximation to the best possible column subset . A key ingredient in our proof is the reduction to the ` p,2-norm , which corresponds to the p-norm of the vector of Euclidean norms of each of the columns of A . This enables us to use strong coreset constructions for Euclidean norms , which p... | The authors present a distributed protocol for the column subset selection problem under the $\ell_p$ norms for $1\leq p <2$. The model of computation is a standard coordinator model of communication where the input matrix A is column partitioned to $s$ servers. The main contribution consists of a protocol that require... | SP:7843f034ba6da2773202b448221de0886c51f545 |
Getting a CLUE: A Method for Explaining Uncertainty Estimates | 1 INTRODUCTION . There is growing interest in probabilistic machine learning models , which aim to provide reliable estimates of uncertainty about their predictions ( MacKay , 1992 ) . These estimates are helpful in highstakes applications such as predicting loan defaults or recidivism , or in work towards autonomous v... | The authors consider the problem of post-hoc explainability for decisions rendered by machine learning models. They focus on addressing uncertain model predictions, producing counterfactual data that is both likely under a generative model of the data, as well as more certain in the classification task. They present ... | SP:51d51aaa3e8df733f5aa3ca3c56dcddf5b3259c4 |
Getting a CLUE: A Method for Explaining Uncertainty Estimates | 1 INTRODUCTION . There is growing interest in probabilistic machine learning models , which aim to provide reliable estimates of uncertainty about their predictions ( MacKay , 1992 ) . These estimates are helpful in highstakes applications such as predicting loan defaults or recidivism , or in work towards autonomous v... | This paper introduces CLUE -- a method to explain uncertainty estimates. The method utilizes a VAE trained on the original data set to search effectively for low confidence instances. The method utilizes a gradient based search through the latent space of the VAE. The authors assess their approach on a variety of ta... | SP:51d51aaa3e8df733f5aa3ca3c56dcddf5b3259c4 |
Getting a CLUE: A Method for Explaining Uncertainty Estimates | 1 INTRODUCTION . There is growing interest in probabilistic machine learning models , which aim to provide reliable estimates of uncertainty about their predictions ( MacKay , 1992 ) . These estimates are helpful in highstakes applications such as predicting loan defaults or recidivism , or in work towards autonomous v... | This paper addresses the problem of explaining the uncertainty of a prediction made by a differentiable probabilistic model (as opposed to the prediction itself) through counterfactual explanations. They propose a technique, CLUE, which optimizes their counterfactual metric in the latent space of a deep generative mode... | SP:51d51aaa3e8df733f5aa3ca3c56dcddf5b3259c4 |
Mixture of Step Returns in Bootstrapped DQN | 1 INTRODUCTION . In recent value-based deep reinforcement learning ( DRL ) , a value function is usually utilized to evaluate state values , which stand for estimates of the expected long-term cumulative rewards that might be collected by an agent . In order to perform such an evaluation , a deep neural network ( DNN )... | This paper combines the idea of multi-step returns for value-based reinforcement learning with Bootstrapped DQN. Updating value functions with different step returns has potential benefits, and was elegantly integrated with the Bootstrapped DQN structure, where different heads have different backup lengths. This work p... | SP:3e683325ee2cea5340d58e08b3fecb67c6e09e7f |
Mixture of Step Returns in Bootstrapped DQN | 1 INTRODUCTION . In recent value-based deep reinforcement learning ( DRL ) , a value function is usually utilized to evaluate state values , which stand for estimates of the expected long-term cumulative rewards that might be collected by an agent . In order to perform such an evaluation , a deep neural network ( DNN )... | This paper utilizes different step return targets for different heads in bootstrapped DQN, such that a more heterogeneous posterior estimation of policy value may be obtained. However, my main concern is the novelty. The step size can be viewed as a tunable hyperparameter, and the posterior computation is mainly credit... | SP:3e683325ee2cea5340d58e08b3fecb67c6e09e7f |
Mixture of Step Returns in Bootstrapped DQN | 1 INTRODUCTION . In recent value-based deep reinforcement learning ( DRL ) , a value function is usually utilized to evaluate state values , which stand for estimates of the expected long-term cumulative rewards that might be collected by an agent . In order to perform such an evaluation , a deep neural network ( DNN )... | This paper proposes Mixture Bootstrapped DQN (MB-DQN). The new algorithm modifies bootstrapped DQN by giving different backup lengths to each head. It shows better performance in several Atari environments, compared to bootstrapped DQN. This work also attempts to analyze the source of empirical benefits in terms of att... | SP:3e683325ee2cea5340d58e08b3fecb67c6e09e7f |
On the Critical Role of Conventions in Adaptive Human-AI Collaboration | 1 INTRODUCTION . Humans collaborate well together in complex tasks by adapting to each other through repeated interactions . What emerges from these repeated interactions is shared knowledge about the interaction history . We intuitively refer to this shared knowledge as conventions . Convention formation helps explain... | This paper makes the observation that when performing cooperative tasks with partners, there are two components to learn: how to perform the task, and how to coordinate with the partner according to conventions. Therefore, it proposes to separate these two components via a modular architecture, which learns a task-spec... | SP:49760213d0f27d6934490916f8300851fd91561e |
On the Critical Role of Conventions in Adaptive Human-AI Collaboration | 1 INTRODUCTION . Humans collaborate well together in complex tasks by adapting to each other through repeated interactions . What emerges from these repeated interactions is shared knowledge about the interaction history . We intuitively refer to this shared knowledge as conventions . Convention formation helps explain... | This paper proposes that in human-AI collaboration using deep neural nets, the AI agents we train should separate learning the _rules_ of the environment from the _conventions_ used to coordinate with humans in that environment. It proposes a simple method to do so: learn a single task-specific module that is always us... | SP:49760213d0f27d6934490916f8300851fd91561e |
On the Critical Role of Conventions in Adaptive Human-AI Collaboration | 1 INTRODUCTION . Humans collaborate well together in complex tasks by adapting to each other through repeated interactions . What emerges from these repeated interactions is shared knowledge about the interaction history . We intuitively refer to this shared knowledge as conventions . Convention formation helps explain... | The paper proposes an interesting model to study multi-agent interactions, in uncertain environments. In s nutshell, the model proposed consists of a MDP (Finite state, action, horizon) and two players playing simultaneously (the turn-by-turn model can be subsumed by the simultaneous move model as stated in the paper).... | SP:49760213d0f27d6934490916f8300851fd91561e |
Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design | 1 INTRODUCTION . A survey paper on MCTS , published in 2012 , has cited 240 papers , including many game and non-game applications ( Browne et al. , 2012 ) . Since the invention of Upper Confidence bound applied to Trees ( UCT ) ( Kocsis & Szepesvári , 2006 ) ( the most representative MCTS algorithm ) in 2006 , MCTS ha... | The paper proposes a new algorithm to scale up parallel MCTS. The proposed method, MP-MCTS is a modified version of previous efforts to parallelize MCTS (TDS-UCT, TDS-df-UCT), all using virtual loss and modern MCTS enhancements (NN-guided selection learned offline). In exchange for small additional memory requirement, ... | SP:c6d8de42cce0118baf2a59b69da17305bf89fb44 |
Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design | 1 INTRODUCTION . A survey paper on MCTS , published in 2012 , has cited 240 papers , including many game and non-game applications ( Browne et al. , 2012 ) . Since the invention of Upper Confidence bound applied to Trees ( UCT ) ( Kocsis & Szepesvári , 2006 ) ( the most representative MCTS algorithm ) in 2006 , MCTS ha... | This paper aims to provide a scalable and competitive molecular design technique based on MCTS. Machine learning guided molecular design is a popular field and many solutions have been developed in the recent years. There is appeal, due to the high dimensionality of search space, to resort to MCTS based approaches that... | SP:c6d8de42cce0118baf2a59b69da17305bf89fb44 |
Practical Massively Parallel Monte-Carlo Tree Search Applied to Molecular Design | 1 INTRODUCTION . A survey paper on MCTS , published in 2012 , has cited 240 papers , including many game and non-game applications ( Browne et al. , 2012 ) . Since the invention of Upper Confidence bound applied to Trees ( UCT ) ( Kocsis & Szepesvári , 2006 ) ( the most representative MCTS algorithm ) in 2006 , MCTS ha... | In this work the authors apply a distributed parallel Monte-Carlo Tree Search (MCTS) algorithm to the Molecular Design problem. The goal of this paper is to speed-up the computation needed by an MCTS algorithm using parallelization over multiple machines. The authors show how to modify a previously successful parallel ... | SP:c6d8de42cce0118baf2a59b69da17305bf89fb44 |
Learning Stochastic Behaviour from Aggregate Data | 1 Introduction In the context of a dynamic system , Aggregate data refers to the data sets that full trajectory of each individual is not available , meaning that there is no known individual level correspondence . Typical examples include data sets collected for DNA evolution , social gathering , density in control pr... | The paper addresses an interesting problem: learning stochastic dynamics from aggregate data. The aggregate data refers to the setting when the data is anonymized. For example, one has data about the location of the birds at different instants in time, but the birds are not labeled and can not be distinguished from eac... | SP:181689080f5bce74c54b4c18f759ba61211ee583 |
Learning Stochastic Behaviour from Aggregate Data | 1 Introduction In the context of a dynamic system , Aggregate data refers to the data sets that full trajectory of each individual is not available , meaning that there is no known individual level correspondence . Typical examples include data sets collected for DNA evolution , social gathering , density in control pr... | The paper is about an interesting setting, where observations correspond to lots of individuals evolving over time. The twist is that individual identifiers are not available. Thus from one time point to the next, the observer does not know which individual is which. The problem is to learn a time series model from th... | SP:181689080f5bce74c54b4c18f759ba61211ee583 |
Learning Stochastic Behaviour from Aggregate Data | 1 Introduction In the context of a dynamic system , Aggregate data refers to the data sets that full trajectory of each individual is not available , meaning that there is no known individual level correspondence . Typical examples include data sets collected for DNA evolution , social gathering , density in control pr... | This paper proposes a new approach to learn the dynamics of density evolution of objects from aggregated data. The basic idea is to derive a closed-form Wasserstein distance between the empirical data distribution and the predicted distribution generated from the weak form of Fokker Planck Equation (FPE). Based on this... | SP:181689080f5bce74c54b4c18f759ba61211ee583 |
Rapid Task-Solving in Novel Environments | 1 INTRODUCTION . An ideal AI system would be useful immediately upon deployment in a new environment , and would become more useful as it gained experience there . Consider for example a household robot deployed in a new home . Ideally , the new owner could turn the robot on and ask it to get started , say , by cleanin... | The authors present the challenge of rapid task-solving (RTS), where the goal is to solve a series of tasks as quickly as possible in a new, shared environment. This challenge requires both memory and planning capabilities from an agent, and the authors demonstrate that current SOTA modern deep RL agents with memory co... | SP:97da780fb0c3a770d3a37d9a183dc8b1b4ba1bd0 |
Rapid Task-Solving in Novel Environments | 1 INTRODUCTION . An ideal AI system would be useful immediately upon deployment in a new environment , and would become more useful as it gained experience there . Consider for example a household robot deployed in a new home . Ideally , the new owner could turn the robot on and ask it to get started , say , by cleanin... | The authors propose a non-parametric memory based on the transformer/self-attention architecture to learn over tasks that require planning from previously experienced tasks. They describe this style of learning as a form of meta reinforcement learning where individual episodes are a collection of tasks in the same envi... | SP:97da780fb0c3a770d3a37d9a183dc8b1b4ba1bd0 |
Rapid Task-Solving in Novel Environments | 1 INTRODUCTION . An ideal AI system would be useful immediately upon deployment in a new environment , and would become more useful as it gained experience there . Consider for example a household robot deployed in a new home . Ideally , the new owner could turn the robot on and ask it to get started , say , by cleanin... | The paper proposes 2 new benchmarks for Rapid Task Solving (RTS), that evaluate RL agents on the ability to memorize past experiences and learn to plan to solve new tasks in different environments rapidly. The paper also proposes Episodic Planning Networks (EPN), an RL method that replaces a weighted sum and multi-laye... | SP:97da780fb0c3a770d3a37d9a183dc8b1b4ba1bd0 |
Nearest Neighbor Machine Translation | 1 INTRODUCTION . Non-parametric methods have recently been successfully applied to tasks such as language modeling ( Khandelwal et al. , 2020 ) and question answering ( Guu et al. , 2020 ; Lewis et al. , 2020 ) . They allow models that are ( 1 ) expressive , because they can use an arbitrary amount of data at test time... | The paper is an extension of [1]. The task in [1] is Language Modeling, while this paper is doing machine translation with the similar idea. The authors propose a non-parametric method for machine translation via a k-nearest-neighbor (KNN) classifier. Specifically, it predicts tokens with a KNN classifier over examples... | SP:97f770b52e8757978d650cd8c82c9cff2ea97d78 |
Nearest Neighbor Machine Translation | 1 INTRODUCTION . Non-parametric methods have recently been successfully applied to tasks such as language modeling ( Khandelwal et al. , 2020 ) and question answering ( Guu et al. , 2020 ; Lewis et al. , 2020 ) . They allow models that are ( 1 ) expressive , because they can use an arbitrary amount of data at test time... | This submission introduces the kNN-MT approach for neural machine translation, which incorporates the memorize-catching spirit with the nearest neighbor classifier on a large datastore when generating the decoding sentences, together with the neural machine translation model for similarity search. No additional paramet... | SP:97f770b52e8757978d650cd8c82c9cff2ea97d78 |
Nearest Neighbor Machine Translation | 1 INTRODUCTION . Non-parametric methods have recently been successfully applied to tasks such as language modeling ( Khandelwal et al. , 2020 ) and question answering ( Guu et al. , 2020 ; Lewis et al. , 2020 ) . They allow models that are ( 1 ) expressive , because they can use an arbitrary amount of data at test time... | This paper describes a nearest-neighbor enhancement to NMT, where internal token-level context representations are used to index into a large data store to find relevant (source, target prefix) pairs. Since the index representation is taken from a pre-softmax representation in the decoder network, no additional trainin... | SP:97f770b52e8757978d650cd8c82c9cff2ea97d78 |
On the Origin of Implicit Regularization in Stochastic Gradient Descent | 1 INTRODUCTION . In the limit of vanishing learning rates , stochastic gradient descent with minibatch gradients ( SGD ) follows the path of gradient flow on the full batch loss function ( Yaida , 2019 ) . However in deep networks , SGD often achieves higher test accuracies when the learning rate is moderately large ( ... | To analyze why the generalization error of SGD with larger learning rates achieves better test error, this paper analyzes the implicit regularization of SGD (with a finite step size) via a first order backward error analysis. Under this analysis the paper shows that the mean position of SGD with $m$ minibatches effecti... | SP:eea0d9d66ccca3fab3cc7077d8e003be5cfcfe29 |
On the Origin of Implicit Regularization in Stochastic Gradient Descent | 1 INTRODUCTION . In the limit of vanishing learning rates , stochastic gradient descent with minibatch gradients ( SGD ) follows the path of gradient flow on the full batch loss function ( Yaida , 2019 ) . However in deep networks , SGD often achieves higher test accuracies when the learning rate is moderately large ( ... | Using backward error analysis, the paper argues that SGD with small but finite step sizes stays on the path of a gradient flow ODE of a modified loss, which penalizes the squared norms of the mini-batch gradients. This offers a possible explanation of the empirically observed positive effect of (relatively) large step ... | SP:eea0d9d66ccca3fab3cc7077d8e003be5cfcfe29 |
On the Origin of Implicit Regularization in Stochastic Gradient Descent | 1 INTRODUCTION . In the limit of vanishing learning rates , stochastic gradient descent with minibatch gradients ( SGD ) follows the path of gradient flow on the full batch loss function ( Yaida , 2019 ) . However in deep networks , SGD often achieves higher test accuracies when the learning rate is moderately large ( ... | This paper analyzes the implicit regularization in SGD with finite learning rates via backward error analysis. The modified flow introduced in this paper better approximates the practical behavior of SGD as it does not require vanishing learning rates and it allows to use random shuffling in stead of i.i.d sampling. Th... | SP:eea0d9d66ccca3fab3cc7077d8e003be5cfcfe29 |
Fourier Representations for Black-Box Optimization over Categorical Variables | 1 INTRODUCTION . A plethora of practical optimization problems involve black-box functions , with no simple analytical closed forms , that can be evaluated at any arbitrary point in the domain . Optimization of such black-box functions poses a unique challenge due to restrictions on the number of possible function eval... | The paper proposes two representations, namely one-hot encoded Boolean expansion and group-theoretical Fourier expansion, for the surrogate model used for the black-box evaluations on purely categorical variables. With the two surrogate models, the authors tackle both the black-box optimization problem and the design p... | SP:d42f0ce82c552abd6dca4300796a3f5678a9409a |
Fourier Representations for Black-Box Optimization over Categorical Variables | 1 INTRODUCTION . A plethora of practical optimization problems involve black-box functions , with no simple analytical closed forms , that can be evaluated at any arbitrary point in the domain . Optimization of such black-box functions poses a unique challenge due to restrictions on the number of possible function eval... | This paper proposes a model-based black-box function optimization on purely categorical variables. Two different representations for categorical variables are proposed, one is an improved pseudo-boolean function form capable of representing non-binary categorical variables in a compact way and another is to rely on (ma... | SP:d42f0ce82c552abd6dca4300796a3f5678a9409a |
Fourier Representations for Black-Box Optimization over Categorical Variables | 1 INTRODUCTION . A plethora of practical optimization problems involve black-box functions , with no simple analytical closed forms , that can be evaluated at any arbitrary point in the domain . Optimization of such black-box functions poses a unique challenge due to restrictions on the number of possible function eval... | The paper considers the problem of black-box optimization of expensive functions defined over categorical variables. A surrogate model-based optimization approach is proposed to tackle this problem. Fourier representations are proposed as surrogate model by treating the categorical input as the direct sum of cyclic gro... | SP:d42f0ce82c552abd6dca4300796a3f5678a9409a |
Training By Vanilla SGD with Larger Learning Rates | 1 INTRODUCTION . We are interested in minimizing a function f : Rd → R with an expectation form : min x∈Rd f ( x ) : = Eξ [ K ( x , ξ ) ] , ( 1a ) where the subscript indicates expectation is computed on the random variable ξ . Specially , if the ξ probability distribution is clear and random variable ξ is uniformly di... | This paper studies the smooth finite-sum problem under suitable conditions in the non-convex case. They show the necessary condition for the minimizer $x^*$ being a point of attraction, and Theorem 1 provides a sufficient condition for the strong minimizer $x^*$ to be a point of strong attraction with high probability.... | SP:03fd27c6a1e0c015dbd52eb08dac258efeaba5b1 |
Training By Vanilla SGD with Larger Learning Rates | 1 INTRODUCTION . We are interested in minimizing a function f : Rd → R with an expectation form : min x∈Rd f ( x ) : = Eξ [ K ( x , ξ ) ] , ( 1a ) where the subscript indicates expectation is computed on the random variable ξ . Specially , if the ξ probability distribution is clear and random variable ξ is uniformly di... | This paper investigates the SGD with constant step size (SGD-CS) on non-conex optimization problems. Theoretically, the paper shows the conditions under which a minimizer $x^*$ is a point of attraction in a local neighborhood under the algorithm SGD-CS with sufficiently small step-size. Furthermore, the paper experimen... | SP:03fd27c6a1e0c015dbd52eb08dac258efeaba5b1 |
Training By Vanilla SGD with Larger Learning Rates | 1 INTRODUCTION . We are interested in minimizing a function f : Rd → R with an expectation form : min x∈Rd f ( x ) : = Eξ [ K ( x , ξ ) ] , ( 1a ) where the subscript indicates expectation is computed on the random variable ξ . Specially , if the ξ probability distribution is clear and random variable ξ is uniformly di... | This paper presents a theoretical analysis of SGD with constant step size (SGD-CS) and presents conditions under which SGD-CS leads to parameter updates that converge to a local minima, including parameters of non-convex functions. The authors then show, in context of some special functions, that the step size can be ... | SP:03fd27c6a1e0c015dbd52eb08dac258efeaba5b1 |
Class Normalization for (Continual)? Generalized Zero-Shot Learning | 1 INTRODUCTION . Zero-shot learning ( ZSL ) aims to understand new concepts based on their semantic descriptions instead of numerous input-output learning pairs . It is a key element of human intelligence and our best machines still struggle to master it ( Ferrari & Zisserman , 2008 ; Lampert et al. , 2009 ; Xian et al... | This paper presents a theoretical justification for normalization in model training on how it affects model performance and training time. It proposes two normalization tricks: normalize + scale trick and attributes normalization trick and apply in the zero-shot image classification task. This paper also shows that two... | SP:10eefcfc059363de145c70b8e711c6583c599ed8 |
Class Normalization for (Continual)? Generalized Zero-Shot Learning | 1 INTRODUCTION . Zero-shot learning ( ZSL ) aims to understand new concepts based on their semantic descriptions instead of numerous input-output learning pairs . It is a key element of human intelligence and our best machines still struggle to master it ( Ferrari & Zisserman , 2008 ; Lampert et al. , 2009 ; Xian et al... | The paper shows that normalization is critical for zero-shot learning (ZSL). In the ZSL randomization is coming from the two sources, attribute and feature. Normalization of the two source helps to reduce the variance. The paper uses an embedding based model where normalize visual feature are projected to the attribut... | SP:10eefcfc059363de145c70b8e711c6583c599ed8 |
Class Normalization for (Continual)? Generalized Zero-Shot Learning | 1 INTRODUCTION . Zero-shot learning ( ZSL ) aims to understand new concepts based on their semantic descriptions instead of numerous input-output learning pairs . It is a key element of human intelligence and our best machines still struggle to master it ( Ferrari & Zisserman , 2008 ; Lampert et al. , 2009 ; Xian et al... | This paper provides a thorough analysis in the perspective of data variances on the widely used normalization tricks in the zero-shot learning research: normalize+scale and attribute normalization. It also demonstrates these tricks are not enough w.r.t. normalizing the variance in a non-linear model and propose a norma... | SP:10eefcfc059363de145c70b8e711c6583c599ed8 |
Adversarial Masking: Towards Understanding Robustness Trade-off for Generalization | 1 INTRODUCTION . Deep neural networks have achieved unprecedented success over a variety of tasks and across different domains . However , studies have shown that neural networks are inherently vulnerable to adversarial examples ( Biggio et al. , 2013 ; Szegedy et al. , 2014 ) . To enhance model robustness against adve... | The paper focuses on the generalization issue with adversarial training that various work has recently demonstrated. The paper studies the role of batch normalization (BN) in adversarial robustness and generalizability. The authors single out the rescaling operator in BN to significantly impact the clean and robustness... | SP:9c57304bbaef17f195d88f66ded5b2d0ee93fba8 |
Adversarial Masking: Towards Understanding Robustness Trade-off for Generalization | 1 INTRODUCTION . Deep neural networks have achieved unprecedented success over a variety of tasks and across different domains . However , studies have shown that neural networks are inherently vulnerable to adversarial examples ( Biggio et al. , 2013 ; Szegedy et al. , 2014 ) . To enhance model robustness against adve... | The paper investigates the role of Batch Normalization (BN) in the generalization of deep networks, and its impact in the trade-off between clean and robust accuracy in adversarially trained networks. The authors demonstrate that the rescaling operations in BN when considered in conjunction with the ReLU activation, se... | SP:9c57304bbaef17f195d88f66ded5b2d0ee93fba8 |
Adversarial Masking: Towards Understanding Robustness Trade-off for Generalization | 1 INTRODUCTION . Deep neural networks have achieved unprecedented success over a variety of tasks and across different domains . However , studies have shown that neural networks are inherently vulnerable to adversarial examples ( Biggio et al. , 2013 ; Szegedy et al. , 2014 ) . To enhance model robustness against adve... | This paper follows the direction of previous work AdvProp (Xie et al. (2020)) and aims to use adversarial training as a regularizer to improve the network generalization on clean data. The authors analyze that the different rescaling operation in the batch normalization layer along with ReLU acts as feature masking/sel... | SP:9c57304bbaef17f195d88f66ded5b2d0ee93fba8 |
Offline Meta-Reinforcement Learning with Advantage Weighting | 1 INTRODUCTION . Meta-reinforcement learning ( meta-RL ) has emerged as a promising strategy for tackling the high sample complexity of reinforcement learning algorithms , when the goal is to ultimately learn many tasks . Meta-RL algorithms exploit shared structure among tasks during meta-training , amortizing the cost... | This paper introduces a new problem setting in meta reinforcement learning, namely metaRL. Here the agent is trained on a fixed offline dataset, which distinguishes it from most metaRL algorithms that interact with the environment during meta training. The authors propose a gradient-based meta learning method (MACAW) t... | SP:0c0358ba5e6e771c7684604f3163516c7f38d6eb |
Offline Meta-Reinforcement Learning with Advantage Weighting | 1 INTRODUCTION . Meta-reinforcement learning ( meta-RL ) has emerged as a promising strategy for tackling the high sample complexity of reinforcement learning algorithms , when the goal is to ultimately learn many tasks . Meta-RL algorithms exploit shared structure among tasks during meta-training , amortizing the cost... | The paper proposes the problem of fully offline meta-RL. Here, the idea is to leverage offline experience from multiple tasks to enable fast adaptation to new tasks. The paper distinguishes two settings of offline meta-RL, one where only the training data is collected offline and testing corresponds to sampling online ... | SP:0c0358ba5e6e771c7684604f3163516c7f38d6eb |
Offline Meta-Reinforcement Learning with Advantage Weighting | 1 INTRODUCTION . Meta-reinforcement learning ( meta-RL ) has emerged as a promising strategy for tackling the high sample complexity of reinforcement learning algorithms , when the goal is to ultimately learn many tasks . Meta-RL algorithms exploit shared structure among tasks during meta-training , amortizing the cost... | This paper proposes a method for "fully" offline meta-RL. Specifically, they assume there is no interaction with the environment at all neither during meta-train nor meta-test and this method only sees previously collected data at all times. Their method is built on top of AWR [1] in which policy updates are weighted b... | SP:0c0358ba5e6e771c7684604f3163516c7f38d6eb |
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference | Recent works have applied Bayesian Neural Network ( BNN ) to adversarial training , and shown the improvement of adversarial robustness via the BNN ’ s strength of stochastic gradient defense . However , we have found that in general , the BNN loses its stochasticity after its training with the BNN ’ s posterior . As a... | In this paper the authors study the adversarial robustness of BNNs on large scale datasets. BNNs have been shown to be a more robust learning paradigm due to their uncertainty/stochasticity. Given the empirical observation that adversarially trained BNN posterior variances converge to zero (which the authors need to do... | SP:820ce414ae58e414bc7008fad4b4ea8fa7e511d1 |
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference | Recent works have applied Bayesian Neural Network ( BNN ) to adversarial training , and shown the improvement of adversarial robustness via the BNN ’ s strength of stochastic gradient defense . However , we have found that in general , the BNN loses its stochasticity after its training with the BNN ’ s posterior . As a... | This paper presents a new adversarial training for BNNs with variational inference (VI). Specifically, Adv-BNN training of Liu et al. 2019 uses a standard normal prior for VI of BNNs. The paper observes that the above method may have vanished stochasticity that reduces the robustness and the proposed method extends it... | SP:820ce414ae58e414bc7008fad4b4ea8fa7e511d1 |
Towards Adversarial Robustness of Bayesian Neural Network through Hierarchical Variational Inference | Recent works have applied Bayesian Neural Network ( BNN ) to adversarial training , and shown the improvement of adversarial robustness via the BNN ’ s strength of stochastic gradient defense . However , we have found that in general , the BNN loses its stochasticity after its training with the BNN ’ s posterior . As a... | This paper studies the adversarial robustness of DNNs with Bayesian neural networks. Although BNN has been integrated with adversarial training for better robustness, this paper argues that the previous method lacks the stochasticity (i.e., the posterior tends to have zero variance), thus limiting the robustness perfor... | SP:820ce414ae58e414bc7008fad4b4ea8fa7e511d1 |
Distributional Generalization: A New Kind of Generalization | 1 INTRODUCTION . We begin with an experiment motivating the need for a notion of generalization beyond test error . Experiment 1 . Consider a binary classification version of CIFAR-10 , where CIFAR-10 images x have binary labels Animal/Object . Take 50K samples from this distribution as a train set , but apply the foll... | This paper proposes an extended notion of generalization. The new proposed notion asks that for a family of tests $T: X \times Y → [0,1]$, $T(x, f(x))$ will be similar for train/test examples. The paper proposes three interesting conjectures that are related to distributional generalization. The paper proves Conjectur... | SP:88f994339e8969434de7fbc81b5b94f6991a636c |
Distributional Generalization: A New Kind of Generalization | 1 INTRODUCTION . We begin with an experiment motivating the need for a notion of generalization beyond test error . Experiment 1 . Consider a binary classification version of CIFAR-10 , where CIFAR-10 images x have binary labels Animal/Object . Take 50K samples from this distribution as a train set , but apply the foll... | This paper proposes a new notion of generalization called distributional generalization which states that the outputs of the classifier for train and test are close as distributions not just their corresponding accuracy numbers. They propose conjectures about their the distributional closeness that they expect and how ... | SP:88f994339e8969434de7fbc81b5b94f6991a636c |
Distributional Generalization: A New Kind of Generalization | 1 INTRODUCTION . We begin with an experiment motivating the need for a notion of generalization beyond test error . Experiment 1 . Consider a binary classification version of CIFAR-10 , where CIFAR-10 images x have binary labels Animal/Object . Take 50K samples from this distribution as a train set , but apply the foll... | This paper introduces a new notion of "distributional generalization" as a tool to quantify the difference between the outputs from training and testing data sets using a certain machine learning algorithm. The authors formulate two conjectures for the so-called "Interpolating classifiers": the Feature Calibration Conj... | SP:88f994339e8969434de7fbc81b5b94f6991a636c |
On Disentangled Representations Extracted from Pretrained GANs | 1 INTRODUCTION . Unsupervised learning of disentangled representations is currently one of the most important challenges in machine learning . Identifying and separating the factors of variation for the data at hand provides a deeper understanding of its internal structure and can bring new insights into the data gener... | This paper proposes a new approach to learn disentangled representations from Generative models that were trained without a loss that explicitly enforces disengagement. The motivation behind this work is to address the computational time cost regarding hyperparameter tuning that is typically required for VAEs trained w... | SP:219fd05784a7cf93f1435a0e541e7b6937df31cd |
On Disentangled Representations Extracted from Pretrained GANs | 1 INTRODUCTION . Unsupervised learning of disentangled representations is currently one of the most important challenges in machine learning . Identifying and separating the factors of variation for the data at hand provides a deeper understanding of its internal structure and can bring new insights into the data gener... | This paper proposes a non-conventional approach to learning disentangled representations through finding interpretable directions in the latent space of a pre-trained Style-GAN (Karras et al., 2018). To find these directions, several existing techniques from the recent literature on controllable image generation are co... | SP:219fd05784a7cf93f1435a0e541e7b6937df31cd |
On Disentangled Representations Extracted from Pretrained GANs | 1 INTRODUCTION . Unsupervised learning of disentangled representations is currently one of the most important challenges in machine learning . Identifying and separating the factors of variation for the data at hand provides a deeper understanding of its internal structure and can bring new insights into the data gener... | This paper proposes a method to learn disentangled representations. The idea is to use an existing GAN generator and use an existing controllable generation algorithm (such as ClosedForm or GANspace) to find a set of “important” directions in the latent space. These subspaces spanned by these directions could already r... | SP:219fd05784a7cf93f1435a0e541e7b6937df31cd |
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning | Optimism in the face of uncertainty is a principled approach for provably efficient exploration for reinforcement learning in tabular and linear settings . However , such an approach is challenging in developing practical exploration algorithms for Deep Reinforcement Learning ( DRL ) . To address this problem , we prop... | This paper studies optimistic exploration for deep reinforcement learning. They propose an algorithm called OEB3, which utilizes the disagreement of bootstrapping Q-functions as the confidence bonus to guide exploration. They show that their confidence bonus matches the confidence bonus of optimistic LSVI in linear set... | SP:72dc93c38fc6cc67f33a291f82dccaf69cc5bdda |
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning | Optimism in the face of uncertainty is a principled approach for provably efficient exploration for reinforcement learning in tabular and linear settings . However , such an approach is challenging in developing practical exploration algorithms for Deep Reinforcement Learning ( DRL ) . To address this problem , we prop... | This paper focuses on deep reinforcement learning and proposes a practical exploration algorithm called Optimistic Exploration algorithm with Backward Bootstrapped Bonus. Based on the Optimistic LSVI algorithm, the authors propose a new optimistic exploration bonus for general cases, similar to the optimistic explorati... | SP:72dc93c38fc6cc67f33a291f82dccaf69cc5bdda |
Optimistic Exploration with Backward Bootstrapped Bonus for Deep Reinforcement Learning | Optimism in the face of uncertainty is a principled approach for provably efficient exploration for reinforcement learning in tabular and linear settings . However , such an approach is challenging in developing practical exploration algorithms for Deep Reinforcement Learning ( DRL ) . To address this problem , we prop... | The paper proposes a UCB-based optimistic exploration method for DRL, called Optimistic Exploration algorithm with Backward Bootstrapped Bonus (OEB3). The algorithm builds on the idea of optimistic exploration developed in the theoretical optimistic LSVI algorithm for linear MDP. In optimistic LSVI, the optimistic Q-va... | SP:72dc93c38fc6cc67f33a291f82dccaf69cc5bdda |
Class2Simi: A New Perspective on Learning with Label Noise | 1 INTRODUCTION . It is expensive to label large-scale data accurately . Therefore , cheap datasets with label noise are ubiquitous in the era of big data . However , label noise will degenerate the performance of trained deep models , because deep networks will easily overfit label noise ( Zhang et al. , 2017 ; Zhong e... | The paper addresses the problem of learning with noisy labels by transforming the original category classification task into a semantic-similarity prediction task. The new task takes pairs of samples as input and predicts if the two samples are coming from the same category or not. It is theoretically shown that the si... | SP:38c530428ad4a8b4bc956998fc9c53a09e1beddf |
Class2Simi: A New Perspective on Learning with Label Noise | 1 INTRODUCTION . It is expensive to label large-scale data accurately . Therefore , cheap datasets with label noise are ubiquitous in the era of big data . However , label noise will degenerate the performance of trained deep models , because deep networks will easily overfit label noise ( Zhang et al. , 2017 ; Zhong e... | This paper proposes a new algorithm on learning noisy datasets by transforming class labels into pairwise similarity labels. It also gives some theoretical analysis on the fact that the induced similarity noise transition matrix works better than the class noise transition matrix. The paper empirically demonstrates tha... | SP:38c530428ad4a8b4bc956998fc9c53a09e1beddf |
Class2Simi: A New Perspective on Learning with Label Noise | 1 INTRODUCTION . It is expensive to label large-scale data accurately . Therefore , cheap datasets with label noise are ubiquitous in the era of big data . However , label noise will degenerate the performance of trained deep models , because deep networks will easily overfit label noise ( Zhang et al. , 2017 ; Zhong e... | This paper presented a working framework for learning a robust classifier with noisy labels. It proves that if the number of the classes is more than 8 then the noise rate in the similarity matrix is less than that in the noise rate in labels. Hence after learning classifiers from noisy labels, it updates the classifie... | SP:38c530428ad4a8b4bc956998fc9c53a09e1beddf |
Overcoming barriers to the training of effective learned optimizers | 1 INTRODUCTION . Much of the success of modern deep learning has been driven by a shift from hand-designed features carefully curated by human experts , to domain-agnostic methods that can learn features from large amounts of data . By leveraging large-scale datasets with flexible models , we are now able to rapidly le... | The goal of a learned optimizer is to replace a human-designed optimizer with a parametric optimizer. However, prior learned optimizers were ineffective at generalizing to a diverse set of tasks. This paper investigates how to learn a useful optimizer by increasing computational scale, building a large, diverse trainin... | SP:cc0f8de167292fb0c4030746628536907cb3913a |
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