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Label Leakage and Protection in Two-party Split Learning | 1 INTRODUCTION . With increasing concerns over data privacy in machine learning , federated learning ( FL ) ( McMahan et al. , 2017 ) has become a promising direction of study . Based on how sensitive data are distributed among parties , FL can be classified into different categories , notable among which are horizonta... | The authors in the paper consider stealing the private label information from the party that does not know the label of training data during split training for binary classification. Specifically, the attack methods are based on differences of gradient information between positive and negative examples and the defense ... | SP:1d660d8b2497c51b08143c85a1969ddd76da2bc4 |
Label Leakage and Protection in Two-party Split Learning | 1 INTRODUCTION . With increasing concerns over data privacy in machine learning , federated learning ( FL ) ( McMahan et al. , 2017 ) has become a promising direction of study . Based on how sensitive data are distributed among parties , FL can be classified into different categories , notable among which are horizonta... | ## Summary of Contributions This paper studies the multiparty setting where there are two parties: the first party (aka non-label party) holds the feature vectors $X_i$ of user $i$ whereas the second party (aka label party) holds the corresponding labels $y_i \in \{0, 1\}$. Together they wish to jointly train a model ... | SP:1d660d8b2497c51b08143c85a1969ddd76da2bc4 |
NeuPL: Neural Population Learning | Learning in strategy games ( e.g . StarCraft , poker ) requires the discovery of diverse policies . This is often achieved by iteratively training new policies against existing ones , growing a policy population that is robust to exploit . This iterative approach suffers from two issues in real-world games : a ) under ... | The paper proposes Neural Population Learning which I think extends PSRO in two aspects. First, it avoid the premature *good*-response. Second, it uses a conditional network to represent the population of policies, so as to enable skill transfer. NeuPL also offers convergence guarantees under some assumptions. NeuPL i... | SP:b9f4ba88299bd3c2ea77354ceff3bfbb788b7a4b |
NeuPL: Neural Population Learning | Learning in strategy games ( e.g . StarCraft , poker ) requires the discovery of diverse policies . This is often achieved by iteratively training new policies against existing ones , growing a policy population that is robust to exploit . This iterative approach suffers from two issues in real-world games : a ) under ... | Population based training (PBT) algorithms progressively grow a set of policies by adding best-responses to mixtures of the existing population. When RL is used as a best-response method the new policy is generally not a best-response but instead a good response. Moreover, the good-response is initialized tabula rosa p... | SP:b9f4ba88299bd3c2ea77354ceff3bfbb788b7a4b |
NeuPL: Neural Population Learning | Learning in strategy games ( e.g . StarCraft , poker ) requires the discovery of diverse policies . This is often achieved by iteratively training new policies against existing ones , growing a policy population that is robust to exploit . This iterative approach suffers from two issues in real-world games : a ) under ... | This submission provides an integrated and versatile NeuPL framework to improve the performance and convergence speed of population-based training algorithms by representing the entire population of policies within a single conditional policy network. NeuPL is very general, the commonly used population-based training a... | SP:b9f4ba88299bd3c2ea77354ceff3bfbb788b7a4b |
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees | Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications , including adversarial learning , GANs , transport and robust optimization . With increasing data and problem sizes necessary to train high performing models across various application... | The paper studies the communication needed in order for a group of distributed players to collectively solve a variational inequality problem. The paper provides two algorithms MASHA1, MASHA2 to do this that solve both the deterministic and stochastic cases. They also provide experimental results on applying their tec... | SP:3ca5d9c117190de8a3ef3b3eb3c495e403b9efd2 |
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees | Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications , including adversarial learning , GANs , transport and robust optimization . With increasing data and problem sizes necessary to train high performing models across various application... | The paper develops a decentralized algorithm for solving variational inequalities with a certain structure motivated by machine learning applications. The key innovation is the utilization of compression (i.e., quantization) for communicating loss functions and their aggregates between a set of devices and a centralize... | SP:3ca5d9c117190de8a3ef3b3eb3c495e403b9efd2 |
Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees | Variational inequalities in general and saddle point problems in particular are increasingly relevant in machine learning applications , including adversarial learning , GANs , transport and robust optimization . With increasing data and problem sizes necessary to train high performing models across various application... | This paper considers the compression methods for solving variational inequalities (or saddle point problems in particular) in the distributed setting. Both unbiased (i.e., MASHA1) and contractive (i.e., MASHA2) compression methods have been proposed. Theoretical analysis is provided to show that the proposed method can... | SP:3ca5d9c117190de8a3ef3b3eb3c495e403b9efd2 |
Generalized Sampling Method for Few Shot Learning | 1 INTRODUCTION . Few-shot learning ( FSL ) refers the problem of learning from datasets where only a limited number of examples ( typically , one to tens per class or a problem in general ) are available for training a machine learning model . FSL has gained importance over the years since obtaining large labelled data... | This paper suggests a data augmentation method for few-shot learning. They generalize the similar method, DC (Yang et al., 2021) introducing a couple of hyperparameters to tune. Like DC, without introducing any learnable parameters, they suggest a way to augment few-shot data by extrapolating its distribution base on w... | SP:d76d871926f67cecd9c47a7215358c4f781216cf |
Generalized Sampling Method for Few Shot Learning | 1 INTRODUCTION . Few-shot learning ( FSL ) refers the problem of learning from datasets where only a limited number of examples ( typically , one to tens per class or a problem in general ) are available for training a machine learning model . FSL has gained importance over the years since obtaining large labelled data... | This paper presents a statistical sampling method to augment related extrapolated features to the novel class few-shot samples by constructing extrapolated distribution with sampled related base classes to construct much robust classifier for few-shot classification. The paper is an improved version over a previous sam... | SP:d76d871926f67cecd9c47a7215358c4f781216cf |
Generalized Sampling Method for Few Shot Learning | 1 INTRODUCTION . Few-shot learning ( FSL ) refers the problem of learning from datasets where only a limited number of examples ( typically , one to tens per class or a problem in general ) are available for training a machine learning model . FSL has gained importance over the years since obtaining large labelled data... | The authors propose a statistical method for estimating the data distribution for few-shot learning. The method is built on a previous work (DC) with minor fixes but have non-trivial performance improvements. Specifically, the main differences lie in the transformation function, weighted random variables and the covari... | SP:d76d871926f67cecd9c47a7215358c4f781216cf |
Truth Table Deep Convolutional Neural Network, A New SAT-Encodable Architecture - Application To Complete Robustness | 1 INTRODUCTION . Deep Neural Network ( DNN ) systems offer exceptional performance in a variety of difficult domains ( Goodfellow et al. , 2016 ) and today these results far outstrip our ability to secure and analyze those DNNs . As DNNs are becoming widely integrated in a variety of applications , several concerns hav... | This paper proposes Truth Table Deep Convolutional Neural Networks (TT-DCNNs), which _distills_ real-valued feature vectors (i.e., small convolutional layers) into truth tables (also called masks), which are represented in boolean formulas using the disjunctive normal form (DNF). These masks indicate certain interpreta... | SP:a12ed5ea62f4a7e8a8f709aa7b425784d77eb84c |
Truth Table Deep Convolutional Neural Network, A New SAT-Encodable Architecture - Application To Complete Robustness | 1 INTRODUCTION . Deep Neural Network ( DNN ) systems offer exceptional performance in a variety of difficult domains ( Goodfellow et al. , 2016 ) and today these results far outstrip our ability to secure and analyze those DNNs . As DNNs are becoming widely integrated in a variety of applications , several concerns hav... | This paper proposes a new kind of SAT-encodable Neural Network which has real-valued weights and binary activations. Since the activations are binary, this work constructs a truth table to map the inputs of a CNN block to its outputs. This helps to deduce the output of the CNN block as a boolean function of the input v... | SP:a12ed5ea62f4a7e8a8f709aa7b425784d77eb84c |
Truth Table Deep Convolutional Neural Network, A New SAT-Encodable Architecture - Application To Complete Robustness | 1 INTRODUCTION . Deep Neural Network ( DNN ) systems offer exceptional performance in a variety of difficult domains ( Goodfellow et al. , 2016 ) and today these results far outstrip our ability to secure and analyze those DNNs . As DNNs are becoming widely integrated in a variety of applications , several concerns hav... | This article proposes a novel convolutional architecture, dubbed Truth Table DCNs (TT-DCNs). Compared to Binary Neural Networks (BNNs), the proposed architecture admits a much more compact propositional logic encoding while improving their accuracy. To the best of my understanding, the core idea is using real-weighte... | SP:a12ed5ea62f4a7e8a8f709aa7b425784d77eb84c |
Where is the bottleneck in long-tailed classification? | 1 INTRODUCTION . Long-tailed distributions are those in which samples from a small number of head ( majority ) classes vastly outnumber the samples for a large number of tail ( minority ) classes . Such distributions can occur naturally , such as rare diseases in medical contexts or minority ethnic groups in face recog... | This paper tries to prove that there is a bottleneck in feature learning for long-tailed classification and data augmentation can help relieve the issues in long-tail feature space. Three major experiments were done to prove that feature space 1) is more biased than balanced feature space, 2) is more disused and less ... | SP:03a10ca1c5c7b5d974354c2d9936bf95f348e3e9 |
Where is the bottleneck in long-tailed classification? | 1 INTRODUCTION . Long-tailed distributions are those in which samples from a small number of head ( majority ) classes vastly outnumber the samples for a large number of tail ( minority ) classes . Such distributions can occur naturally , such as rare diseases in medical contexts or minority ethnic groups in face recog... | This paper poses an interesting and important question - where are the bottlenecks in long-tailed classification. The authors use empirical experiments to show their observations: (1) representation is more critical than classifier, (2) data augmentation is helpful. Three datasets (CIFAR-10 LT, CIFAR-100 LT and ImageN... | SP:03a10ca1c5c7b5d974354c2d9936bf95f348e3e9 |
Where is the bottleneck in long-tailed classification? | 1 INTRODUCTION . Long-tailed distributions are those in which samples from a small number of head ( majority ) classes vastly outnumber the samples for a large number of tail ( minority ) classes . Such distributions can occur naturally , such as rare diseases in medical contexts or minority ethnic groups in face recog... | This paper seeks to study what is the bottleneck in long-tailed learning. Based on extensive experiments, the authors propose that representation learning is the bottleneck in long-tailed classification. Also, this paper analyzes representation learning from the perspectives of intra-class compactness and inter-class s... | SP:03a10ca1c5c7b5d974354c2d9936bf95f348e3e9 |
Model Compression via Symmetries of the Parameter Space | 1 INTRODUCTION . Recent work has shown that representation theory , the formal study of symmetry , provides the foundation for various innovative techniques in deep learning ( Cohen & Welling , 2016 ; Kondor & Trivedi , 2018 ; Ravanbakhsh et al. , 2017 ; Cohen & Welling , 2017 ) . Much of this previous work considers s... | The authors make use of a theoretical framework for viewing neural networks as representation of quivers to introduce a reparametrization strategy for neural networks with radial activation functions. This reparametrization is based on a QR decomposition, and leads to a lossless compression of the number of parameters ... | SP:bed075c80d6a879ab7960c2cf4b4c44a53e917ce |
Model Compression via Symmetries of the Parameter Space | 1 INTRODUCTION . Recent work has shown that representation theory , the formal study of symmetry , provides the foundation for various innovative techniques in deep learning ( Cohen & Welling , 2016 ; Kondor & Trivedi , 2018 ; Ravanbakhsh et al. , 2017 ; Cohen & Welling , 2017 ) . Much of this previous work considers s... | This paper provides a framework to study compression of NN models by analyses of symmetry properties of their parameter spaces. For lossless model compression, an algorithm employing QR decomposition for parameter compression is proposed. In the theoretical analyses, the decomposition of the proposed neural quiver is s... | SP:bed075c80d6a879ab7960c2cf4b4c44a53e917ce |
Model Compression via Symmetries of the Parameter Space | 1 INTRODUCTION . Recent work has shown that representation theory , the formal study of symmetry , provides the foundation for various innovative techniques in deep learning ( Cohen & Welling , 2016 ; Kondor & Trivedi , 2018 ; Ravanbakhsh et al. , 2017 ; Cohen & Welling , 2017 ) . Much of this previous work considers s... | This paper presents a dimensionality reduction technique that can preserve the model of a neural network by leveraging radial symmetry. Consequently, when the required conditions apply, they also show that training the compressed model is equivalent to training the original model on a particular projection of the param... | SP:bed075c80d6a879ab7960c2cf4b4c44a53e917ce |
Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference | 1 INTRODUCTION . Continual learning ( CL ) is a learning scenario where a model learns from a continuous and online stream of data and is regarded as a more realistic and practical learning setup than offline learning on a fixed dataset ( He et al. , 2020 ) . However , many CL methods still focus on the offline setup (... | The paper proposes a more realistic setting (called *i-Blurry*) for continual learning (CL) that generalizes the *blurry* and *disjoint* settings proposed in prior work. The disjoint setting assumes that there is no class that appears in multiple tasks and the blurry setting assumes that no new classes are seen after ... | SP:8955f90191ee97eeb451e79dc12cd7921f6c3fd5 |
Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference | 1 INTRODUCTION . Continual learning ( CL ) is a learning scenario where a model learns from a continuous and online stream of data and is regarded as a more realistic and practical learning setup than offline learning on a fixed dataset ( He et al. , 2020 ) . However , many CL methods still focus on the offline setup (... | In this paper, the authors proposed a new benchmark protocol (i-Blurry) for continual learning. In this benchmark protocol, the class distribution is class incremental and has blurry task boundaries, and the training is online. They also propose a new method, CILB. This method contains three important components: “samp... | SP:8955f90191ee97eeb451e79dc12cd7921f6c3fd5 |
Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference | 1 INTRODUCTION . Continual learning ( CL ) is a learning scenario where a model learns from a continuous and online stream of data and is regarded as a more realistic and practical learning setup than offline learning on a fixed dataset ( He et al. , 2020 ) . However , many CL methods still focus on the offline setup (... | The paper proposes a new problem setup in continual learning. As the title suggests, the paper focuses on online, task-free, class incremental, task blurry learning with any-time inference. The authors also came up with new baselines and importance-based memory management. They empirically tested their methods in the p... | SP:8955f90191ee97eeb451e79dc12cd7921f6c3fd5 |
Active Learning over Multiple Domains in Natural Language Tasks | 1 INTRODUCTION . New natural language problems , outside the watershed of core NLP , are often strictly limited by a dearth of labeled data . While unlabeled data is frequently available , it is not always from the same source as the target distribution . This is particularly prevalent for tasks characterized by ( i ) ... | This paper makes comparison with techniques used in active learning (AL), domain shift detection (DS), and multi-domain sampling to combine data from multiple sources. The experiments are conducted on datasets from questions answering and sentiment analysis. The paper is well organized and easy to follow. However, th... | SP:9bddccfae5ba4235b81fd964f91249da855520fe |
Active Learning over Multiple Domains in Natural Language Tasks | 1 INTRODUCTION . New natural language problems , outside the watershed of core NLP , are often strictly limited by a dearth of labeled data . While unlabeled data is frequently available , it is not always from the same source as the target distribution . This is particularly prevalent for tasks characterized by ( i ) ... | This paper surveys a broad range of techniques for active learning in the multi-domain setting applied to text - specifically, given a small labeled dataset from a target domain and a large amount of unlabeled data from a collection of source domains, how should examples be picked from the source domains to for labelin... | SP:9bddccfae5ba4235b81fd964f91249da855520fe |
Active Learning over Multiple Domains in Natural Language Tasks | 1 INTRODUCTION . New natural language problems , outside the watershed of core NLP , are often strictly limited by a dearth of labeled data . While unlabeled data is frequently available , it is not always from the same source as the target distribution . This is particularly prevalent for tasks characterized by ( i ) ... | The authors investigate the efficacy of several active learning-related techniques in classification under domain shift with multiple source domains. They include: uncertainty methods, H-divergence methods, reverse-classification methods, and nearest neighbor methods. They construct 18 methods with various combination... | SP:9bddccfae5ba4235b81fd964f91249da855520fe |
Contrastive Clustering to Mine Pseudo Parallel Data for Unsupervised Translation | Modern unsupervised machine translation systems mostly train their models by generating synthetic parallel training data from large unlabeled monolingual corpora of different languages through various means , such as iterative backtranslation . However , there may exist small amount of actual parallel data hidden in th... | This work provides a nice extension to the SwAV proposed by Caron. Specifically, it proposes a variant that can be used to build meaningful cluster assignment for different languages at the same time in an unsupervised manner. The key to their modification to SwAv is the so called language-agnostic rebalancing function... | SP:db23298f9c982e7e2f54f4e57175295b131d0fe8 |
Contrastive Clustering to Mine Pseudo Parallel Data for Unsupervised Translation | Modern unsupervised machine translation systems mostly train their models by generating synthetic parallel training data from large unlabeled monolingual corpora of different languages through various means , such as iterative backtranslation . However , there may exist small amount of actual parallel data hidden in th... | The authors propose a language agnostic SwAV loss function to mine pseudo parallel data from unlabeled monolingual corpus from various languages. They start with a pretrained masked language model and then fine-tune it using their proposed language agnostic SwAV. The self supervised model predicts cluster code based on... | SP:db23298f9c982e7e2f54f4e57175295b131d0fe8 |
Contrastive Clustering to Mine Pseudo Parallel Data for Unsupervised Translation | Modern unsupervised machine translation systems mostly train their models by generating synthetic parallel training data from large unlabeled monolingual corpora of different languages through various means , such as iterative backtranslation . However , there may exist small amount of actual parallel data hidden in th... | This paper presents improvements of unsupervised machine translation (UMT). It introduces a modified SwAV loss to cluster sentences from monolingual data that are then exploited during training of UMT. Evaluation is performed on standard UMT tasks and an in-depth analysis is provided. | SP:db23298f9c982e7e2f54f4e57175295b131d0fe8 |
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training | 1 INTRODUCTION . The existence of adversarial examples ( Szegedy et al. , 2014 ) causes serious safety concerns when deploying modern deep learning models . For example , for classification tasks , imperceptible perturbations of the input instance can fool state-of-the-art classifiers . Many strategies to obtain models... | This paper proposes a metric to measure the difficulty of training examples and find that hard training examples influence the generalization of adversarial training and cause overfitting in adversarial training. The authors also provide a theoretical analysis. To mitigate the issue caused by hard training examples, th... | SP:5f2dd029e11b05daf0eb2a07c33aad57bb8fe982 |
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training | 1 INTRODUCTION . The existence of adversarial examples ( Szegedy et al. , 2014 ) causes serious safety concerns when deploying modern deep learning models . For example , for classification tasks , imperceptible perturbations of the input instance can fool state-of-the-art classifiers . Many strategies to obtain models... | This paper began by analyzing the influence of hard adversarial examples. And, they found that hard adversarial examples may be the major reason leading to robust overfitting. Then, based on these observations and analysis, the authors further introduced a Fast Adversarial Training scheme, which can achieve competitive... | SP:5f2dd029e11b05daf0eb2a07c33aad57bb8fe982 |
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training | 1 INTRODUCTION . The existence of adversarial examples ( Szegedy et al. , 2014 ) causes serious safety concerns when deploying modern deep learning models . For example , for classification tasks , imperceptible perturbations of the input instance can fool state-of-the-art classifiers . Many strategies to obtain models... | This paper proposes a metric for measuring the difficulty of instances. The authors empirically show that hard instances lead to the issue of robust overfitting. Theoretical analysis on logistic regression and general nonlinear model indicates that as the increase of instance difficulty, the adversarial vulnerability b... | SP:5f2dd029e11b05daf0eb2a07c33aad57bb8fe982 |
Defending Against Image Corruptions Through Adversarial Augmentations | 1 INTRODUCTION . By following a process known as Empirical Risk Minimization ( ERM ) ( Vapnik , 1998 ) , neural networks are trained to minimize the average error on a training set . ERM has enabled breakthroughs in a wide variety of fields and applications ( Goodfellow et al. , 2016 ; Krizhevsky et al. , 2012 ; Hinton... | The authors propose a new data augmentation technique, called AdversarialAugment, to increase robustness of image classification models. The proposed method optimizes the parameters of image-to-image models to generate adversarially corrupted images, where they also show sufficient conditions for the consistency in the... | SP:32b52a8f3f6cfbcd0a863d6ea84dc691f504a3b2 |
Defending Against Image Corruptions Through Adversarial Augmentations | 1 INTRODUCTION . By following a process known as Empirical Risk Minimization ( ERM ) ( Vapnik , 1998 ) , neural networks are trained to minimize the average error on a training set . ERM has enabled breakthroughs in a wide variety of fields and applications ( Goodfellow et al. , 2016 ; Krizhevsky et al. , 2012 ; Hinton... | The main contribution of the paper is the proposed AdversarialAugment (AdA) method. AdA generates augmented versions of an input by passing the input through an corruption network (such as a pretrained image-to-image model) while adding a worst-case perturbation to the _weights_ of this pretrained network. The paper th... | SP:32b52a8f3f6cfbcd0a863d6ea84dc691f504a3b2 |
Defending Against Image Corruptions Through Adversarial Augmentations | 1 INTRODUCTION . By following a process known as Empirical Risk Minimization ( ERM ) ( Vapnik , 1998 ) , neural networks are trained to minimize the average error on a training set . ERM has enabled breakthroughs in a wide variety of fields and applications ( Goodfellow et al. , 2016 ; Krizhevsky et al. , 2012 ; Hinton... | This paper provides a data augmentation method that augments samples by perturbing parameters of a generative model. The perturbations are found by an adversarial loss, and are constrained based on a perceptual similarity distance to guard from the outliers. In addition to thorough empirical evaluations, this paper pro... | SP:32b52a8f3f6cfbcd0a863d6ea84dc691f504a3b2 |
Fishr: Invariant Gradient Variances for Out-of-distribution Generalization | 1 INTRODUCTION . The success of deep neural networks in supervised learning ( Krizhevsky et al. , 2012 ) relies on the crucial assumption that the train and test data distributions are identical . In particular , the tendency of networks to rely on simple features ( Kalimeris et al. , 2019 ; Valle-Perez et al. , 2019 ;... | This paper introduces a simple technique for out-of-distribution generalization, called Fishr. Intuitively, given the success of CORAL, Fishr enforces consistency through mean regularization between the element-wise variances of the gradients across domains. It is efficiently solvable using the existing BackPACK packag... | SP:633112bc888148bb86867601dd21c422e55e31a5 |
Fishr: Invariant Gradient Variances for Out-of-distribution Generalization | 1 INTRODUCTION . The success of deep neural networks in supervised learning ( Krizhevsky et al. , 2012 ) relies on the crucial assumption that the train and test data distributions are identical . In particular , the tendency of networks to rely on simple features ( Kalimeris et al. , 2019 ; Valle-Perez et al. , 2019 ;... | This paper proposes a new regularization method for OOD generalization. The central idea is to align the covariances of gradients between different domains. Specifically, - Authors first categorize different works in this area into three groups: 1. Methods that match **features** across domains such as those in adv... | SP:633112bc888148bb86867601dd21c422e55e31a5 |
Fishr: Invariant Gradient Variances for Out-of-distribution Generalization | 1 INTRODUCTION . The success of deep neural networks in supervised learning ( Krizhevsky et al. , 2012 ) relies on the crucial assumption that the train and test data distributions are identical . In particular , the tendency of networks to rely on simple features ( Kalimeris et al. , 2019 ; Valle-Perez et al. , 2019 ;... | This paper proposes a regularization for Out-of-Distribution Generalization problem, which aims to align the gradient of different domains. Extensive experiments are performed to validate the effectiveness of the proposed method. And some intuitions are also given to demonstrate the Fishr method. | SP:633112bc888148bb86867601dd21c422e55e31a5 |
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity | 1 INTRODUCTION . Despite it being a relatively new subfield of machine learning ( ML ) , Federated Learning ( FL ) ( McMahan et al. , 2017 ; Reddi et al. , 2021 ; Horvath et al. , 2021 ) has become an indispensable tool to enable privacy-preserving collaboratively learning , as well as to deliver personalised models ta... | The paper investigates sparse training to accelerate on-device federated learning. It begins by taking an off-the-shelf sparse training method (SWAT) and analyzes its limitations. Then, the method proposes three strategies to improve upon SWAT. Experiments are conducted on CIFAR-10 dataset and show some improvements co... | SP:3dc0bafc12ceb8a9387173cb7640049e4f9c62ce |
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity | 1 INTRODUCTION . Despite it being a relatively new subfield of machine learning ( ML ) , Federated Learning ( FL ) ( McMahan et al. , 2017 ; Reddi et al. , 2021 ; Horvath et al. , 2021 ) has become an indispensable tool to enable privacy-preserving collaboratively learning , as well as to deliver personalised models ta... | The paper studies the problem of adding sparsity to local training in federated learning. Under the approach each node uses only the top-K weights for computations in the forward and backward pass of training (and only top-K activations for computations in the backward pass). The authors empirically show that adding sp... | SP:3dc0bafc12ceb8a9387173cb7640049e4f9c62ce |
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity | 1 INTRODUCTION . Despite it being a relatively new subfield of machine learning ( ML ) , Federated Learning ( FL ) ( McMahan et al. , 2017 ; Reddi et al. , 2021 ; Horvath et al. , 2021 ) has become an indispensable tool to enable privacy-preserving collaboratively learning , as well as to deliver personalised models ta... | This work proposes ZeroFL; a method that allows for sparse neural network training at the edge along with reduced upload communication, both of which being important aspects in federated learning. ZeroFL essentially follows a prior work on sparse neural network training, namely SWAT, and adapts it appropriately for th... | SP:3dc0bafc12ceb8a9387173cb7640049e4f9c62ce |
Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System | 1 INTRODUCTION . Continual learning ( CL ) refers to the ability of a learning agent to continuously interact with a dynamic environment and process a stream of information to acquire new knowledge while consolidating and retaining previously obtained knowledge ( Parisi et al. , 2019 ) . This ability to continuously le... | The paper presents CLS-ER, a dual memory mechanism for the continual learning setting. During training the model receives data from a non-i.i.d. source as well as random samples from a sample buffer (the episodic memory). The stable and plastic models act as teacher models. Their logits are the target of the student mo... | SP:9b693bad5fc10f11cc5942ee023fd4ec37a6f964 |
Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System | 1 INTRODUCTION . Continual learning ( CL ) refers to the ability of a learning agent to continuously interact with a dynamic environment and process a stream of information to acquire new knowledge while consolidating and retaining previously obtained knowledge ( Parisi et al. , 2019 ) . This ability to continuously le... | They propose a dual memory experience based on complementary learning systems (CLS). The working model is updated using consistency loss with the selected optimal semantic memory from plastic and stable models. Plastic and stable models are updated with an exponential moving average trick (EMA) and the working model. | SP:9b693bad5fc10f11cc5942ee023fd4ec37a6f964 |
Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System | 1 INTRODUCTION . Continual learning ( CL ) refers to the ability of a learning agent to continuously interact with a dynamic environment and process a stream of information to acquire new knowledge while consolidating and retaining previously obtained knowledge ( Parisi et al. , 2019 ) . This ability to continuously le... | In this paper, motivated from the CLS theory in neuroscience that efficient learning requires short-term adaptation and slow learning of structured information, the authors propose a novel dual memory experience replay method for continual learning. The idea is to build long-term and short-term semantic memories by ma... | SP:9b693bad5fc10f11cc5942ee023fd4ec37a6f964 |
Evolution Strategies as an Alternate Learning method for Hierarchical Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) has been used to create artificially intelligent agents for tasks ranging from robot locomotion ( Haarnoja et al. , 2018 ) to video games such as StarCraft ( Vinyals et al. , 2019 ) and board games such as chess and Go ( Silver et al. , 2018 ) . Many such agents use Markov... | This paper proposes a novel approach for hierarchical reinforcement learning. As the title says, the authors claims that the use of evolution strategies is useful to train hierarchical policy. Basically, the search is based on the estimation of the gradients of the controller fitness and the primitive fitness using the... | SP:c145dd1a3c7e89293956d6a13e3597ccf33973c2 |
Evolution Strategies as an Alternate Learning method for Hierarchical Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) has been used to create artificially intelligent agents for tasks ranging from robot locomotion ( Haarnoja et al. , 2018 ) to video games such as StarCraft ( Vinyals et al. , 2019 ) and board games such as chess and Go ( Silver et al. , 2018 ) . Many such agents use Markov... | The paper presents the integration of evolution strategies with hierarchical reinforcement learning. From the natural characteristics of ES algorithms, it also can be highly scalable, so authors named the algorithm as SHES (Scalable Hierarchical Evolution Strategies). The algorithm is tested in two robot locomotion and... | SP:c145dd1a3c7e89293956d6a13e3597ccf33973c2 |
Evolution Strategies as an Alternate Learning method for Hierarchical Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) has been used to create artificially intelligent agents for tasks ranging from robot locomotion ( Haarnoja et al. , 2018 ) to video games such as StarCraft ( Vinyals et al. , 2019 ) and board games such as chess and Go ( Silver et al. , 2018 ) . Many such agents use Markov... | This paper proposes to apply evolution strategy to hierarchical reinforcement learning. The high-level controller sets goals and the low-level primitive learns to move to the goal. The high-level and the low-level have different reward functions. The paper applies evolution strategy to solve such a problem. The propose... | SP:c145dd1a3c7e89293956d6a13e3597ccf33973c2 |
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series | 1 INTRODUCTION . In the past years , methods such as ( Salinas et al. , 2020 ; Franceschi et al. , 2019 ; Kurle et al. , 2020 ; de Bézenac et al. , 2020 ; Oreshkin et al. , 2019 ; Rasul et al. , 2021 ; Cui et al. , 2016 ; Wang et al. , 2016 ) have consistently showcased their effectiveness of deep learning in time ser... | The authors propose a new GAN-based algorithm for time series synthesis. They use progressive growing of GAN architectures to improve the performance of GAN and self-attention to enhance the expressive capability of neural networks. The experimental results validate the superiority of the proposed PSA-GAN algorithm. | SP:017769783f4bdb1c409591da5299bf7d037f2bad |
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series | 1 INTRODUCTION . In the past years , methods such as ( Salinas et al. , 2020 ; Franceschi et al. , 2019 ; Kurle et al. , 2020 ; de Bézenac et al. , 2020 ; Oreshkin et al. , 2019 ; Rasul et al. , 2021 ; Cui et al. , 2016 ; Wang et al. , 2016 ) have consistently showcased their effectiveness of deep learning in time ser... | The paper proposes a type of GAN to generate synthetic time series. The authors use the data generated by their proposed model to train forecasting networks, which improves the baselines. They also show that the proposed GAN can itself be used as a forecasting model and that it has competitive performance to baseline ... | SP:017769783f4bdb1c409591da5299bf7d037f2bad |
PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series | 1 INTRODUCTION . In the past years , methods such as ( Salinas et al. , 2020 ; Franceschi et al. , 2019 ; Kurle et al. , 2020 ; de Bézenac et al. , 2020 ; Oreshkin et al. , 2019 ; Rasul et al. , 2021 ; Cui et al. , 2016 ; Wang et al. , 2016 ) have consistently showcased their effectiveness of deep learning in time ser... | This paper proposes a GAN-based approach (PSA) for generating realistic time-series data that can be used to improve several downstream tasks such as imputation of missing data and forecasting. The generator and discrimination model in the proposed GAN framework, PSA-GAN consists of progressively growing blocks consist... | SP:017769783f4bdb1c409591da5299bf7d037f2bad |
Invariant Causal Mechanisms through Distribution Matching | 1 INTRODUCTION . Learning structured representations which capture the underlying causal mechanisms generating data is of central importance for training robust machine learning models ( Bengio et al. , 2013 ; Schölkopf et al. , 2021 ) . One particular structure the learned representation should capture is invariance t... | - The paper claims to provide a unifying causal framework for invariance-based algorithms. - It uses the graphical model in figure 1 to derive some conditions for achieving invariance. - It also develops a new algorithm for invariance-based representation learning. The idea is based on conjecture 4.3, which hypothesize... | SP:ebd310cc9bc1e6d543022b8d75dbd1dc9e3c819c |
Invariant Causal Mechanisms through Distribution Matching | 1 INTRODUCTION . Learning structured representations which capture the underlying causal mechanisms generating data is of central importance for training robust machine learning models ( Bengio et al. , 2013 ; Schölkopf et al. , 2021 ) . One particular structure the learned representation should capture is invariance t... | This paper considers a causal framework for invariant representation learning, with some analysis about the invariance and independences based on the proposed causal graph. An algorithm is then proposed based on the above analysis and empirical result validates the performance of the proposed algorithm. The paper is we... | SP:ebd310cc9bc1e6d543022b8d75dbd1dc9e3c819c |
Invariant Causal Mechanisms through Distribution Matching | 1 INTRODUCTION . Learning structured representations which capture the underlying causal mechanisms generating data is of central importance for training robust machine learning models ( Bengio et al. , 2013 ; Schölkopf et al. , 2021 ) . One particular structure the learned representation should capture is invariance t... | This work provides a causal perspective and new algorithm for learning invariant representations from multiple domain datasets. This work introduces the notion of style variables and shows theoretically that being invariant to the domain index actually leads to invariance to the style variables. The extensive experimen... | SP:ebd310cc9bc1e6d543022b8d75dbd1dc9e3c819c |
GANet: Glyph-Attention Network for Few-Shot Font Generation | 1 Introduction . With the increasing popularity of Chinese characters globally , the demand for Chinese fonts is rising . However , it ’ s very time consuming and expensive to design such font library . As we know , the number of letters in English alphabet is 52 ( upper and lower cases ) , much smaller than that of th... | The paper proposes a glyph-attention network for few-shot font generation. They claim that fonts’ content features are basically global features and style features are related to local features. They propose a style glyph attention to capture the global features from the content set of glyphs, and a content glyph atten... | SP:c50e58518244f90f768aa571d1bc4485dc9a5eec |
GANet: Glyph-Attention Network for Few-Shot Font Generation | 1 Introduction . With the increasing popularity of Chinese characters globally , the demand for Chinese fonts is rising . However , it ’ s very time consuming and expensive to design such font library . As we know , the number of letters in English alphabet is 52 ( upper and lower cases ) , much smaller than that of th... | This paper considers Chinese glyph font style transfer problem with few reference inputs. The model has three encoders for query, style and content references; one decoder for target generation; and two discriminators for style and content. The main novelty comes with the glyph attention design with both local and glob... | SP:c50e58518244f90f768aa571d1bc4485dc9a5eec |
GANet: Glyph-Attention Network for Few-Shot Font Generation | 1 Introduction . With the increasing popularity of Chinese characters globally , the demand for Chinese fonts is rising . However , it ’ s very time consuming and expensive to design such font library . As we know , the number of letters in English alphabet is 52 ( upper and lower cases ) , much smaller than that of th... | This paper proposed a few-shot font generation method, GANet. The key idea is to design glyph-attention modules including the style glyph-attention module and the content glyph-attention module to recover the glyph in the target style from the queried glyph. The multi-task adversarial loss was also employed to further ... | SP:c50e58518244f90f768aa571d1bc4485dc9a5eec |
Data Poisoning Won’t Save You From Facial Recognition | 1 INTRODUCTION . Facial recognition systems pose a serious threat to individual privacy . Various companies routinely scrape the Web for users ’ pictures to train large-scale facial recognition systems ( Hill , 2020a ; Harwell , 2021 ) , and then make these systems available to law enforcement agencies ( Lipton , 2020 ... | This paper studies the effect of data poisoning in face recognition and the relation to the defense techniques. Conventionally, the poisoned data will fail the face recognition models who is trained without defense strategy. Two solutions of defense are given: oblivious trainer and adaptive trainer. The claim is that, ... | SP:23cca476cbbe185c5c3f88c910817da2ff6d6458 |
Data Poisoning Won’t Save You From Facial Recognition | 1 INTRODUCTION . Facial recognition systems pose a serious threat to individual privacy . Various companies routinely scrape the Web for users ’ pictures to train large-scale facial recognition systems ( Hill , 2020a ; Harwell , 2021 ) , and then make these systems available to law enforcement agencies ( Lipton , 2020 ... | This paper points out that current data poisoning techniques cannot effiectively protect users privacy, i.e., face data, on the Internet. The authors have examined several strategies to enable modern face recognition models to defense attacks from widely used data poisoning methods. Experimental results suggest that th... | SP:23cca476cbbe185c5c3f88c910817da2ff6d6458 |
Data Poisoning Won’t Save You From Facial Recognition | 1 INTRODUCTION . Facial recognition systems pose a serious threat to individual privacy . Various companies routinely scrape the Web for users ’ pictures to train large-scale facial recognition systems ( Hill , 2020a ; Harwell , 2021 ) , and then make these systems available to law enforcement agencies ( Lipton , 2020 ... | Recent works propose to protect users from facial recognition by poisoning their images before uploading them to the Internet (called poisoning "attacks"). This paper reveals the flaws of these methods by designing two effective methods (called "defenses") to defeat that protection mechanism. The first approach is adap... | SP:23cca476cbbe185c5c3f88c910817da2ff6d6458 |
Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond | 1 INTRODUCTION . Discovering new drugs to fulfill specific criteria , such as binding affinity towards a given molecular target , is a fundamental problem in chemistry and the pharmaceutical industry ( Hughes et al. , 2011 ) . In this work , we focus on an important subdomain : de novo biological sequence design . This... | The paper draws on connections between likelihood-free inference and black-box optimization to propose new black-box optimization methods. In general, the goal here is to not find the exact optimum of the black-box objective, but to sample from a set of sequences with high-quality objective. This is akin to the problem... | SP:9367c02ee25747aa6ab8131ef68171b717d1f9ea |
Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond | 1 INTRODUCTION . Discovering new drugs to fulfill specific criteria , such as binding affinity towards a given molecular target , is a fundamental problem in chemistry and the pharmaceutical industry ( Hughes et al. , 2011 ) . In this work , we focus on an important subdomain : de novo biological sequence design . This... | The authors describe a mapping from likelihood free inference to black-box sequence optimization, then use this mapping to link common algorithms in both fields. They go on to describe novel black-box sequence design algorithms induced by known LFI algorithms. Empirical results show their methods are competitive on sta... | SP:9367c02ee25747aa6ab8131ef68171b717d1f9ea |
Unifying Likelihood-free Inference with Black-box Sequence Design and Beyond | 1 INTRODUCTION . Discovering new drugs to fulfill specific criteria , such as binding affinity towards a given molecular target , is a fundamental problem in chemistry and the pharmaceutical industry ( Hughes et al. , 2011 ) . In this work , we focus on an important subdomain : de novo biological sequence design . This... | In this paper the authors draw direct parallels between likelihood-free inference (LFI) and black-box sequence design. This allows that authors to draw parallels between existing methods from the LFI and black-box sequence design literatures. In a few cases there is no direct analog in the black-box sequence design l... | SP:9367c02ee25747aa6ab8131ef68171b717d1f9ea |
Towards Understanding Generalization via Decomposing Excess Risk Dynamics | 1 INTRODUCTION . Generalization is one of the essential mysteries uncovered in modern machine learning ( Neyshabur et al. , 2014 ; Zhang et al. , 2016 ; Kawaguchi et al. , 2017 ) , measuring how the trained model performs on unseen data . One of the most popular approaches to generalization is uniform convergence ( Moh... | This work proposes a new bound for the excess risk based on a decomposition into a bias and variance term. The authors use a stability-based analysis to bound the bias excess risk, and uniform convergence to bound the variance excess risk. They illustrate their framework in two contexts: overparametrised linear regress... | SP:5af29ac70568ac6e8afb37add204d02b534880fe |
Towards Understanding Generalization via Decomposing Excess Risk Dynamics | 1 INTRODUCTION . Generalization is one of the essential mysteries uncovered in modern machine learning ( Neyshabur et al. , 2014 ; Zhang et al. , 2016 ; Kawaguchi et al. , 2017 ) , measuring how the trained model performs on unseen data . One of the most popular approaches to generalization is uniform convergence ( Moh... | This paper proposes a new approach to characterize the excess risk by decomposing the risk into two parts, the risk of learning the exact response and the risk of fitting the noise signal. Under the linear regression setting, the learning dynamic can be decomposed exactly into the proposed two parts. Stability and unif... | SP:5af29ac70568ac6e8afb37add204d02b534880fe |
Towards Understanding Generalization via Decomposing Excess Risk Dynamics | 1 INTRODUCTION . Generalization is one of the essential mysteries uncovered in modern machine learning ( Neyshabur et al. , 2014 ; Zhang et al. , 2016 ; Kawaguchi et al. , 2017 ) , measuring how the trained model performs on unseen data . One of the most popular approaches to generalization is uniform convergence ( Moh... | This paper studies the generalization performance of learning algorithms. The basic idea is to decompose the training dynamics into noise and signal components, and then consider separately the behavior of models trained with noise and signals. The authors then proposed to use the stability approach to tackle the noise... | SP:5af29ac70568ac6e8afb37add204d02b534880fe |
On the Capacity and Superposition of Minima in Neural Network Loss Function Landscapes | 1 INTRODUCTION . Deep learning with neural networks ( NNs ) is a high-dimensional , non-convex optimisation problem for a loss function landscape ( LFL ) . The coordinates of a minimum in the LFL are a set of weights for the machine learning model and a locally optimal solution to the learning problem , and these terms... | The paper proposes a method to select a subset of local minima of loss function landscape and combine the minima using a second meta-network which learns to select a set of weights for each input data point. The subset of local minima is selected based on two criteria, minima that are distant in classification space an... | SP:2ded0ffd70950d61fe69e808c3624b2ccac70b58 |
On the Capacity and Superposition of Minima in Neural Network Loss Function Landscapes | 1 INTRODUCTION . Deep learning with neural networks ( NNs ) is a high-dimensional , non-convex optimisation problem for a loss function landscape ( LFL ) . The coordinates of a minimum in the LFL are a set of weights for the machine learning model and a locally optimal solution to the learning problem , and these terms... | This paper studies a form of ensembling of under-parameterized networks. They show that different local minima specialize distinct subset of inputs, and an algorithm called MLSUP can be used to combine them to improve the prediction. The method is empirical tested with a 1-hidden-layer network on a 2D synthetic classif... | SP:2ded0ffd70950d61fe69e808c3624b2ccac70b58 |
On the Capacity and Superposition of Minima in Neural Network Loss Function Landscapes | 1 INTRODUCTION . Deep learning with neural networks ( NNs ) is a high-dimensional , non-convex optimisation problem for a loss function landscape ( LFL ) . The coordinates of a minimum in the LFL are a set of weights for the machine learning model and a locally optimal solution to the learning problem , and these terms... | ## Summary This paper proposes a new ensemble learning based on neural networks. First, A basin-hopping method is used to sample diverse minima of the empirical risk landscape. Then, a meta-network is trained to predict which minimum performs the best for specific input. This meta-network maps the input sample to the ... | SP:2ded0ffd70950d61fe69e808c3624b2ccac70b58 |
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning | Artificial neural systems trained using reinforcement , supervised , and unsupervised learning all acquire internal representations of high dimensional input . To what extent these representations depend on the different learning objectives is largely unknown . Here we compare the representations learned by eight diffe... | This paper compares the representations learned by otherwise identical networks (up to the output layer) for different tasks: supervised (4 tasks), unsupervised (autoencoders, vanilla and variational), self-supervised predictive coding, untrained (randomly initialized), and one RL policy network. They are all trained o... | SP:1e7159e897b786fef0cd494a49191673fde611de |
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning | Artificial neural systems trained using reinforcement , supervised , and unsupervised learning all acquire internal representations of high dimensional input . To what extent these representations depend on the different learning objectives is largely unknown . Here we compare the representations learned by eight diffe... | This work identifies the important problem of transfer learning for problems that require deep learning due to high-dimensional inputs. It notes that transfer learning approaches often cannot transfer very far across learning objectives and are judged empirically as black boxes, making for brittle approaches. It propos... | SP:1e7159e897b786fef0cd494a49191673fde611de |
Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning | Artificial neural systems trained using reinforcement , supervised , and unsupervised learning all acquire internal representations of high dimensional input . To what extent these representations depend on the different learning objectives is largely unknown . Here we compare the representations learned by eight diffe... | The paper explores how similar the representations learned by supervised, unsupervised, and RL techniques are in an egocentric virtual rodent environment. The main claim is that representations in RL-trained networks are most different from supervised and unsupervised networks, which is tested with an RSA metric on 4 l... | SP:1e7159e897b786fef0cd494a49191673fde611de |
Optimal Representations for Covariate Shift | Machine learning systems often experience a distribution shift between training and testing . In this paper , we introduce a simple variational objective whose optima are exactly the set of all representations on which risk minimizers are guaranteed to be robust to any distribution shift that preserves the Bayes predic... | The paper studies representation learning under covariate shift. Under the IDG setting and the proposed assumptions, the paper gives a so-called variational characterization of the optimal representation. This characterization shows that the optimal representation should remain discriminative while has the same support... | SP:b6d498d546af2429df5d021ec957ed63170cdc21 |
Optimal Representations for Covariate Shift | Machine learning systems often experience a distribution shift between training and testing . In this paper , we introduce a simple variational objective whose optima are exactly the set of all representations on which risk minimizers are guaranteed to be robust to any distribution shift that preserves the Bayes predic... | This work focuses on learning representations for domain generalization. It proposes to minimize the idealized domain generalization risk (IDG risk; defined on page 2). Under several assumptions (importantly, domain covering augmentations), the IDG risk objective can be altered (Thm.1 -> Prop.2 -> Eq.(6)) to a more pra... | SP:b6d498d546af2429df5d021ec957ed63170cdc21 |
Optimal Representations for Covariate Shift | Machine learning systems often experience a distribution shift between training and testing . In this paper , we introduce a simple variational objective whose optima are exactly the set of all representations on which risk minimizers are guaranteed to be robust to any distribution shift that preserves the Bayes predic... | At a high level, this work considers the problem of designing machine learning systems that generalize well even when the "domain" (i.e., the data-generating process) under which the system is tested (the "target") does not match that under which it was trained (the "source"). More specifically, the authors look to pro... | SP:b6d498d546af2429df5d021ec957ed63170cdc21 |
Goal Randomization for Playing Text-based Games without a Reward Function | 1 INTRODUCTION . Text-based games are complex , interactive simulations in which the game state is described with text and players act using simple text commands ( e.g. , take sandwich from table , eat sandwich , open door , etc . ) ( Côté et al. , 2018 ) . They serve as a proxy for studying how agents can exploit la... | This paper introduces GoalRand, an algorithm for playing text-based games in the absence of extrinsic reward. In particular, it uses generated goals to train a goal-conditioned reinforcement learning agent. These goals are generated by extracting factual information from the agent's knowledge graph. Given a goal sample... | SP:9b1286437860daa22f9fa88d1e04fed46d0784f8 |
Goal Randomization for Playing Text-based Games without a Reward Function | 1 INTRODUCTION . Text-based games are complex , interactive simulations in which the game state is described with text and players act using simple text commands ( e.g. , take sandwich from table , eat sandwich , open door , etc . ) ( Côté et al. , 2018 ) . They serve as a proxy for studying how agents can exploit la... | The authors propose a simple intrinsic reward for text-based games, in the absence of environment rewards. The method, GoalRand, is based on uniformly sampling random goals from a set of natural language goals generated using common-sense rules. The authors evaluate their method on both seen and unseen text-based games... | SP:9b1286437860daa22f9fa88d1e04fed46d0784f8 |
Goal Randomization for Playing Text-based Games without a Reward Function | 1 INTRODUCTION . Text-based games are complex , interactive simulations in which the game state is described with text and players act using simple text commands ( e.g. , take sandwich from table , eat sandwich , open door , etc . ) ( Côté et al. , 2018 ) . They serve as a proxy for studying how agents can exploit la... | This paper studies text-based games using a technique called goal randomization. This method uses random basic goals to train a policy in the absence of the reward of environment. Authors show that agents can learn policies that generalize well across different text-based games. It also seems working better than a stat... | SP:9b1286437860daa22f9fa88d1e04fed46d0784f8 |
Efficient and Modular Implicit Differentiation | 1 INTRODUCTION . Automatic differentiation ( autodiff ) is now an inherent part of machine learning software . It allows to express complex computations by composing elementary ones in creative ways and removes the tedious burden of computing their derivatives by hand . In parallel , the differentiation of optimization... | This paper provides a unified tool for combining the implicit differentiation technique and the automatic differentiation method widely used in existing deep learning packages such as PyTorch and TensorFlow. The proposed implementation is easy to use for numeric optimization such as bilevel optimization, meta-learning... | SP:ddc796b9185d372f4d0829f436bbca50c3990867 |
Efficient and Modular Implicit Differentiation | 1 INTRODUCTION . Automatic differentiation ( autodiff ) is now an inherent part of machine learning software . It allows to express complex computations by composing elementary ones in creative ways and removes the tedious burden of computing their derivatives by hand . In parallel , the differentiation of optimization... | The paper proposes a modular and efficient framework along with its JAX implementation for the implicit differentiation of optimization problems. The user defines the function F capturing the optimality conditions of the problem to be differentiated; then the framework combines implicit differentiation and autodiff of ... | SP:ddc796b9185d372f4d0829f436bbca50c3990867 |
Efficient and Modular Implicit Differentiation | 1 INTRODUCTION . Automatic differentiation ( autodiff ) is now an inherent part of machine learning software . It allows to express complex computations by composing elementary ones in creative ways and removes the tedious burden of computing their derivatives by hand . In parallel , the differentiation of optimization... | This paper introduces a Jax package for implicitly differentiating various numerical solvers. Concretely, the authors develop a systemic methodology for producing gradients for a variety of optimization problems. Then, the authors prove that the Jacobian solution to the approximate numerical solution produces close eno... | SP:ddc796b9185d372f4d0829f436bbca50c3990867 |
NAFS: A Simple yet Tough-to-Beat Baseline for Graph Representation Learning | 1 INTRODUCTION . In recent years , graph representation learning has been extensively applied in various application scenarios , such as node clustering , link prediction , node classification , and graph classification ( Kipf & Welling , 2016b ; a ; Hamilton et al. , 2017 ; Bo et al. , 2020 ; Hettige et al. , 2020 ; W... | The paper presents NAFS (Node-Adaptive Feature Smoothing), a method that constructs node representations by relying on smoothing only, i.e. without parameter learning. To do this, the authors first provide a formulation for the smoothing operator after infinite steps, i.e. when the stationary state is reached. They the... | SP:3c5d850f257a0150def7087735e463d418160a04 |
NAFS: A Simple yet Tough-to-Beat Baseline for Graph Representation Learning | 1 INTRODUCTION . In recent years , graph representation learning has been extensively applied in various application scenarios , such as node clustering , link prediction , node classification , and graph classification ( Kipf & Welling , 2016b ; a ; Hamilton et al. , 2017 ; Bo et al. , 2020 ; Hettige et al. , 2020 ; W... | The authors of this paper took a novel perspective to present the node-adaptive feature smoothing (NAFS) algorithm, which generates node embeddings without explicit training/parameter learning. The method first performs feature smoothing, then combines the smoothed features using adaptive weights which are node-specifi... | SP:3c5d850f257a0150def7087735e463d418160a04 |
NAFS: A Simple yet Tough-to-Beat Baseline for Graph Representation Learning | 1 INTRODUCTION . In recent years , graph representation learning has been extensively applied in various application scenarios , such as node clustering , link prediction , node classification , and graph classification ( Kipf & Welling , 2016b ; a ; Hamilton et al. , 2017 ; Bo et al. , 2020 ; Hettige et al. , 2020 ; W... | The paper deals with (unsupervised) learning with graphs, specifically node-level tasks. Inspired by spectral GNNs, specifically Graph Convolutional Networks (GCN), the authors propose a simple neighborhood smoothing technique to capture the graph structure around each node in the given graph. Contrary to GNN, the prop... | SP:3c5d850f257a0150def7087735e463d418160a04 |
Entroformer: A Transformer-based Entropy Model for Learned Image Compression | 1 INTRODUCTION . Image compression is a fundamental research field in computer vision . With the development of deep learning , learned methods have led to several breakthroughs in this task . Currently , the state-ofthe-art ( SOTA ) deep image compression models are built on the auto-encoder framework ( Hinton & Salak... | This work introduces a transformer-based entropy coding model for learned image compression. The backbones of the commonly used hyperprior encoder and decoder are replaced with transformer encoder layers, and the context model is replaced with the transformer decoder. The striking features of the proposed method includ... | SP:58ce187d0a0ffb7bf0697c4c3b6f2fdd989596c1 |
Entroformer: A Transformer-based Entropy Model for Learned Image Compression | 1 INTRODUCTION . Image compression is a fundamental research field in computer vision . With the development of deep learning , learned methods have led to several breakthroughs in this task . Currently , the state-ofthe-art ( SOTA ) deep image compression models are built on the auto-encoder framework ( Hinton & Salak... | This paper addresses the problem of learned image compression using a transformer as the entropy model. The authors introduce a diamond-shaped relative position encoding scheme that makes sense for image modeling. They also adopt a two-step, bidirectional context model based on a checkerboard-style spatial decompositio... | SP:58ce187d0a0ffb7bf0697c4c3b6f2fdd989596c1 |
Entroformer: A Transformer-based Entropy Model for Learned Image Compression | 1 INTRODUCTION . Image compression is a fundamental research field in computer vision . With the development of deep learning , learned methods have led to several breakthroughs in this task . Currently , the state-ofthe-art ( SOTA ) deep image compression models are built on the auto-encoder framework ( Hinton & Salak... | The authors propose a transformer-based entropy modeling order to capture long-range dependencies in probability distribution estimation. This model is optimized for image compression. The authors extend this architecture with a parallel bidirectional context model to speed up the decoding process. | SP:58ce187d0a0ffb7bf0697c4c3b6f2fdd989596c1 |
Self-Supervised Structured Representations for Deep Reinforcement Learning | Recent reinforcement learning ( RL ) methods have found extracting high-level features from raw pixels with self-supervised learning to be effective in learning policies . However , these methods focus on learning global representations of images , and disregard local spatial structures present in the consecutively sta... | This paper proposes a representation learning method that leverages unsupervised signals like flow and forward models and the constraints between them and apply it to the state representation problem of RL. The method is adding a number of auxiliary rewards to the torso which flow and a latent transition model that a... | SP:c3b0cf6571db40a084f09fa66e3cd74e4b30b783 |
Self-Supervised Structured Representations for Deep Reinforcement Learning | Recent reinforcement learning ( RL ) methods have found extracting high-level features from raw pixels with self-supervised learning to be effective in learning policies . However , these methods focus on learning global representations of images , and disregard local spatial structures present in the consecutively sta... | In this paper, the authors focus on the problem of representation learning for deep reinforcement learning. To this end, they propose an approach to learning structured representations via establishing flows between latent volumes. Similar to SPR, they predict future representations with a transition model conditioned ... | SP:c3b0cf6571db40a084f09fa66e3cd74e4b30b783 |
Self-Supervised Structured Representations for Deep Reinforcement Learning | Recent reinforcement learning ( RL ) methods have found extracting high-level features from raw pixels with self-supervised learning to be effective in learning policies . However , these methods focus on learning global representations of images , and disregard local spatial structures present in the consecutively sta... | This paper focuses on self-supervised visual representation learning for RL applications. As opposed to previous literature that focused on learning global representations for the current observation, the authors propose learning "structured" representations based on flow maps that encode local structure. To do so, the... | SP:c3b0cf6571db40a084f09fa66e3cd74e4b30b783 |
Safe Linear-Quadratic Dual Control with Almost Sure Performance Guarantee | 1 INTRODUCTION . One of the most fundamental and well-studied problems in optimal control , Linear-Quadratic Regulation ( LQR ) has recently aroused renewed interest in the context of data-driven control and reinforcement learning . Considering it is usually challenging to obtain an exact system model from first princi... | This paper addresses the unconstrained stochastic linear-quadratic dual control problem with online parameter identification in an online setting, where a near-optimal control policy is sought to minimize the infinite-horizon quadratic cost (i.e. stabilizing the linear dynamical system) while learning the initially unk... | SP:01927b25e3408a5e889122868c0eee692be69e02 |
Safe Linear-Quadratic Dual Control with Almost Sure Performance Guarantee | 1 INTRODUCTION . One of the most fundamental and well-studied problems in optimal control , Linear-Quadratic Regulation ( LQR ) has recently aroused renewed interest in the context of data-driven control and reinforcement learning . Considering it is usually challenging to obtain an exact system model from first princi... | This paper studies an adaptive control for LQR and provides an algorithm to converge to the optimal policy almost surely in an asymptotic sense, where the convergence rate is also provided. The paper assumes that the system is open-loop stable, and switches to zero control input (with exponentially decaying excitation ... | SP:01927b25e3408a5e889122868c0eee692be69e02 |
Safe Linear-Quadratic Dual Control with Almost Sure Performance Guarantee | 1 INTRODUCTION . One of the most fundamental and well-studied problems in optimal control , Linear-Quadratic Regulation ( LQR ) has recently aroused renewed interest in the context of data-driven control and reinforcement learning . Considering it is usually challenging to obtain an exact system model from first princi... | This paper addresses the problem of combining system identification and optimal control in an online framework for stable linear systems with full-state sensing. Despite the restrictions of linearity, stability, and full-state sensing, the proposed problem formulation is still challenging and I am unaware of any result... | SP:01927b25e3408a5e889122868c0eee692be69e02 |
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data | 1 INTRODUCTION . The mammalian brain is a complex , high-dimensional system , containing billions of neurons whose coordinated dynamics ultimately drives behaviour . Identifying and interpreting these dynamics is the focus of a large body of neuroscience research , which is being facilitated by the advent of new experi... | This paper presents a new approach for inference in a model that simultaneously provides latent dynamics, initial conditions, and - importantly - external inputs. This approach is enabled by using the outcome of an optimization algorithm (iLQR) in the recognition model, recently enabled by other work in the field. The ... | SP:c0d0cff3b0191686d9dae0eaecfe0019711a3966 |
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data | 1 INTRODUCTION . The mammalian brain is a complex , high-dimensional system , containing billions of neurons whose coordinated dynamics ultimately drives behaviour . Identifying and interpreting these dynamics is the focus of a large body of neuroscience research , which is being facilitated by the advent of new experi... | The paper proposes a control-based variational inference approach that learns latent neural dynamics in input-driven SSM. It utilizes iLQR in the recognition model that transforms it into an optimal-control problem. The recognition model in the proposed method is implicitly implied by the generative model and thus redu... | SP:c0d0cff3b0191686d9dae0eaecfe0019711a3966 |
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data | 1 INTRODUCTION . The mammalian brain is a complex , high-dimensional system , containing billions of neurons whose coordinated dynamics ultimately drives behaviour . Identifying and interpreting these dynamics is the focus of a large body of neuroscience research , which is being facilitated by the advent of new experi... | This paper proposes ILQR-VAE, a novel method that allows to simultaneously learn latent dynamics and infer unobserved control inputs. The method relies on IQLR solver and recent advances allowing for implicit differentiation to maximize an Evidence Lower Bound on log-likelihood of observation to infer a conditional dis... | SP:c0d0cff3b0191686d9dae0eaecfe0019711a3966 |
One for Many: an Instagram inspired black-box adversarial attack | 1 INTRODUCTION . It is well known that deep learning models are susceptible to adversarial attacks and many recent researches in the field have been devoted to produce ever more reliable and effective attacks . Attack reliability is strictly connected to its applicability in real-world scenarios and to its ability to b... | This paper introduces a new family of black-box adversarial attacks. These attacks are constructed by composing Instagram filters-based transformation in the input space. Perturbations in the input space are unrestricted and large but only produce natural-looking artifacts. The input is transformed with 10 different fi... | SP:c350b2552deb7ab187ea3cb387f18a8b1789f2e0 |
One for Many: an Instagram inspired black-box adversarial attack | 1 INTRODUCTION . It is well known that deep learning models are susceptible to adversarial attacks and many recent researches in the field have been devoted to produce ever more reliable and effective attacks . Attack reliability is strictly connected to its applicability in real-world scenarios and to its ability to b... | The paper proposes a black-box attack method that uses an evolutionary algorithm to find the best image filter parameters that can achieve untargeted attacks. Model ensembling is also used in the AE generation to improve attack transferability. The proposed method is compared to other similar image filtering-based AE g... | SP:c350b2552deb7ab187ea3cb387f18a8b1789f2e0 |
One for Many: an Instagram inspired black-box adversarial attack | 1 INTRODUCTION . It is well known that deep learning models are susceptible to adversarial attacks and many recent researches in the field have been devoted to produce ever more reliable and effective attacks . Attack reliability is strictly connected to its applicability in real-world scenarios and to its ability to b... | This paper proposed a nested evolutionary algorithm to generate adversarial perturbations under the black-box settings. Such perturbations are composed of various image filters inspired by Instagram and can simultaneously attack multiple neural networks. They claimed that the attacks were semantically robust and had a ... | SP:c350b2552deb7ab187ea3cb387f18a8b1789f2e0 |
ANOMALY DETECTION WITH FRAME-GROUP ATTENTION IN SURVEILLANCE VIDEOS | 1 INTRODUCTION . Nowadays anomaly detection is useful to maintain social security and conduct legal forensics . Due to the ambiguous definition of abnormal events , it increases the difficulty of detection . For example , the appearance of a vehicle on a road is normal , while it is abnormal when a vehicle is on the si... | This paper proposes a novel neural network that maps short chunks of eight video frames to a final probability score (0-1) of abnormality. The network is trained and evaluated in a supervised fashion, using the Avenue, UMN and UCSD data (all three datasets capture semantic anomalies e.g. walking in opposite directions ... | SP:d9735ed582c13df8aa906746db500b271759a785 |
ANOMALY DETECTION WITH FRAME-GROUP ATTENTION IN SURVEILLANCE VIDEOS | 1 INTRODUCTION . Nowadays anomaly detection is useful to maintain social security and conduct legal forensics . Due to the ambiguous definition of abnormal events , it increases the difficulty of detection . For example , the appearance of a vehicle on a road is normal , while it is abnormal when a vehicle is on the si... | The paper presents an algorithm to detect abnormal events in video sequences. The proposed algorithm decomposes a video sequence into groups of consecutive frames ("frame groups"), and uses ConvLSTM to extract features from frame groups. A group level attention module is applied to focus on some most relevant portions ... | SP:d9735ed582c13df8aa906746db500b271759a785 |
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