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Graph-Based Continual Learning | 1 INTRODUCTION . Recent breakthroughs of deep neural networks often hinge on the ability to repeatedly iterate over stationary batches of training data . When exposed to incrementally available data from non-stationary distributions , such networks often fail to learn new information without forgetting much of its prev... | The paper proposes a novel way of using random graphs to improve task-free continual learning method. It builds to random graphs, G and A, based on the similarity of images stored in the memory and those of the current tasks, and utilize the relative information to build representation of the images and predict. The id... | SP:4e23c046f8234b35d88e3957b0725fb7a3d06374 |
Active Learning in CNNs via Expected Improvement Maximization | 1 INTRODUCTION . Deep learning models ( LeCun et al. , 2015 ) have achieved remarkable performance on many challenging prediction tasks , with applications spanning computer vision ( Voulodimos et al. , 2018 ) , computational biology ( Angermueller et al. , 2016 ) , and natural language processing ( Socher et al. , 201... | The paper proposes a method for pool-based active learning in CNNs, selecting the next (batch of) data from an unlabeled pool to query their labels to expedite the learning process. The method computes the expected reduction in the predictive variance across a representative set of points and selects the next data poin... | SP:f7ff3ea337acc5902d501c41e453471c43887711 |
Active Learning in CNNs via Expected Improvement Maximization | 1 INTRODUCTION . Deep learning models ( LeCun et al. , 2015 ) have achieved remarkable performance on many challenging prediction tasks , with applications spanning computer vision ( Voulodimos et al. , 2018 ) , computational biology ( Angermueller et al. , 2016 ) , and natural language processing ( Socher et al. , 201... | The paper considers the problem of active learning for training convolutional neural networks (CNN) in a sample-efficient manner. The proposed approach is built upon the existing idea of selecting points that maximally reduce expected mean squared error (MSE) on a large representative sample of points. MC-dropout is us... | SP:f7ff3ea337acc5902d501c41e453471c43887711 |
Hindsight Curriculum Generation Based Multi-Goal Experience Replay | 1 INTRODUCTION . Multi-goal tasks with sparse rewards present a big challenge for training a reliable RL agent . In multi-goal tasks ( Plappert et al. , 2018 ) , an agent learns to achieve multiple different goals and receives no positive feedback until it reaches the position defined by the desired goal . Such a spars... | This paper focuses on the problem of goal conditioned reinforcement learning. The authors propose an alternative way of performing Bellman updates for goal conditioned value function. Specifically, the proposed method first partitions the space of state goal pairs by performing a K-means clustering, and then estimates ... | SP:3f20bfebcca1b1ff3743a3427ad44221c71e598a |
Hindsight Curriculum Generation Based Multi-Goal Experience Replay | 1 INTRODUCTION . Multi-goal tasks with sparse rewards present a big challenge for training a reliable RL agent . In multi-goal tasks ( Plappert et al. , 2018 ) , an agent learns to achieve multiple different goals and receives no positive feedback until it reaches the position defined by the desired goal . Such a spars... | This paper developed methods for resampling from the hindsight experience replay buffer. The resampling strategy was developed based on the current policy, and the overall distribution of the relative goals. As the distribution over goals evolves over time, the multi-goal agent's replay curriculum is adjusted throughou... | SP:3f20bfebcca1b1ff3743a3427ad44221c71e598a |
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization | 1 INTRODUCTION . Normalizing flows ( Dinh et al. , 2014 ; Rezende & Mohamed , 2015 ) have recently gathered much interest within the machine learning community , ever since its recent breakthrough in modelling high dimensional image data ( Dinh et al. , 2017 ; Kingma & Dhariwal , 2018 ) . They are characterized by an i... | This paper proposes the flow based representation of a probability distribution so that the corresponding density remains tractable. In particular, the push-forward map that generates the desired distribution is characterized by the gradient of a strongly convex potential function. The invertability of the mapping as w... | SP:00578dd55a640c10dbf22f647b736e49f6ee3c32 |
Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization | 1 INTRODUCTION . Normalizing flows ( Dinh et al. , 2014 ; Rezende & Mohamed , 2015 ) have recently gathered much interest within the machine learning community , ever since its recent breakthrough in modelling high dimensional image data ( Dinh et al. , 2017 ; Kingma & Dhariwal , 2018 ) . They are characterized by an i... | The authors introduce CP-Flows, a way to parameterize normalizing flows by constructing an input-convex neural net with softplus-type activation functions and considering its gradient as the flow. They add a quadratic term to ensure invertibility. Using convex optimization techniques their method only needs access to c... | SP:00578dd55a640c10dbf22f647b736e49f6ee3c32 |
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network | Recently , Frankle & Carbin ( 2019 ) demonstrated that randomly-initialized dense networks contain subnetworks that once found can be trained to reach test accuracy comparable to the trained dense network . However , finding these high performing trainable subnetworks is expensive , requiring iterative process of train... | The paper proposes an innovate method based on lottery ticket hypothesis to prune a BNN (parameters are only -1(0) and +1, it can be viewed as an extreme case of quantization) from a dense NN. It focuses on learning a mask to prune the NN instead of the traditional method (pruning on an already trained network). In add... | SP:34c5488e2ff0ef69e35e7000998cd1f105774c33 |
Multi-Prize Lottery Ticket Hypothesis: Finding Accurate Binary Neural Networks by Pruning A Randomly Weighted Network | Recently , Frankle & Carbin ( 2019 ) demonstrated that randomly-initialized dense networks contain subnetworks that once found can be trained to reach test accuracy comparable to the trained dense network . However , finding these high performing trainable subnetworks is expensive , requiring iterative process of train... | The authors propose a stronger lottery ticket hypothesis in this paper – the multi-prize lottery ticket hypothesis. In particular, the new hypothesis seeks answer to the required amount of over-parameterization for a randomly initialized network to become able to compress to a sparse untrained binary subnetwork with on... | SP:34c5488e2ff0ef69e35e7000998cd1f105774c33 |
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders | 1 INTRODUCTION . The goal of scientific image analysis is to analyze pixel-data and measure the properties of objects of interest in images . Pixel intensities are subject to undesired noise and other distortions , motivating an initial preprocessing step called image restoration . Image restoration is the task of remo... | This paper proposes a new method of noise removal using convolutional VAE. An observed image with noise is input to VAE, and after the expression $z$ in the latent space, the noise removed image is finally output. After that, it is possible to generate a pseudo noisy observation image according to the noise model. T... | SP:5d41c9d8df0e7ce2dc9328a938e0f4c9cf2b3bd6 |
Fully Unsupervised Diversity Denoising with Convolutional Variational Autoencoders | 1 INTRODUCTION . The goal of scientific image analysis is to analyze pixel-data and measure the properties of objects of interest in images . Pixel intensities are subject to undesired noise and other distortions , motivating an initial preprocessing step called image restoration . Image restoration is the task of remo... | This paper devises a novel unsupervised denoising paradigm, DIVNOISING, that allows us, for the first time, to generate diverse and plausible denoising solutions, sampled from a learned posterior. This approach only requires noisy images and a suitable description of the imaging noise distribution, providing a new pers... | SP:5d41c9d8df0e7ce2dc9328a938e0f4c9cf2b3bd6 |
Success-Rate Targeted Reinforcement Learning by Disorientation Penalty | Current reinforcement learning generally uses discounted return as its learning objective . However , real-world tasks may often demand a high success rate , which can be quite different from optimizing rewards . In this paper , we explicitly formulate the success rate as an undiscounted form of return with { 0 , 1 } -... | The authors propose a new set-up for reinforcement learning which considers undiscounted episodic returns and introduces a loop-penalty to ensure that all episodes terminate and that the returns are bounded. In the tabular case, the loop-penalty zeros out the reward if a loop is detected in the current episode. For con... | SP:060eedc158b8ebcd2a593500513c1055e1ca158b |
Success-Rate Targeted Reinforcement Learning by Disorientation Penalty | Current reinforcement learning generally uses discounted return as its learning objective . However , real-world tasks may often demand a high success rate , which can be quite different from optimizing rewards . In this paper , we explicitly formulate the success rate as an undiscounted form of return with { 0 , 1 } -... | This paper proposes an alternative surrogate objective for optimizing success rate in episodic tasks with bounded time horizon. Rather than optimize a discounted 0-1 loss (say with discount factor 0.99), the authors suggest to optimize the undiscounted 0-1 loss where reward is counted only for trajectories that do not ... | SP:060eedc158b8ebcd2a593500513c1055e1ca158b |
Learning Curves for Analysis of Deep Networks | A learning curve models a classifier ’ s test error as a function of the number of training samples . Prior works show that learning curves can be used to select model parameters and extrapolate performance . We investigate how to use learning curves to analyze the impact of design choices , such as pretraining , archi... | This paper advocates for studying the effect of design choices in deep learning via their effect on entire *learning curves* (test error vs num samples N), as opposed to their effect only for a fixed N. This is a valid and important message, and it is indeed an aspect that is often overlooked in certain domains. Howeve... | SP:503051a1584c3f2fe519fb8154c63b9066bb4a26 |
Learning Curves for Analysis of Deep Networks | A learning curve models a classifier ’ s test error as a function of the number of training samples . Prior works show that learning curves can be used to select model parameters and extrapolate performance . We investigate how to use learning curves to analyze the impact of design choices , such as pretraining , archi... | In this paper, the authors first propose a simple weighted least squares method to compute the "learning curve" (error plotted against dataset size) , where error is modelled with the form error = alpha - eta*n^gamma, for parameters alpha, eta, gamma. Gamma is taken to be - 0.5, while alpha, eta are estimated from the ... | SP:503051a1584c3f2fe519fb8154c63b9066bb4a26 |
Whitening and second order optimization both destroy information about the dataset, and can make generalization impossible | 1 INTRODUCTION . Whitening is a data preprocessing step that removes correlations between input features ( see Fig . 1 ) . It is used across many scientific disciplines , including geology ( Gillespie et al. , 1986 ) , physics ( Jenet et al. , 2005 ) , machine learning ( Le Cun et al. , 1998 ) , linguistics ( Abney , 2... | In supervised learning tasks, it is common in practice to apply a *whitening* transformation to remove correlations between input features. This can improve the conditioning of the underlying data manifold, enabling faster convergence. This paper shows that for a large class of models --- models $f$ consisting of a fu... | SP:5ad585e3d6d5f9f620d0d73d958de52ed90255e0 |
Whitening and second order optimization both destroy information about the dataset, and can make generalization impossible | 1 INTRODUCTION . Whitening is a data preprocessing step that removes correlations between input features ( see Fig . 1 ) . It is used across many scientific disciplines , including geology ( Gillespie et al. , 1986 ) , physics ( Jenet et al. , 2005 ) , machine learning ( Le Cun et al. , 1998 ) , linguistics ( Abney , 2... | The authors analyse the training dynamics of a machine learning model consisting in a linear unit, followed by any parametrized function. The authors in particular focus on the impact of whitening the data beforehand or using second order methods. They show that the learned parameters of the model only depend on the t... | SP:5ad585e3d6d5f9f620d0d73d958de52ed90255e0 |
Self-Supervised Policy Adaptation during Deployment | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has achieved considerable success when combined with convolutional neural networks for deriving actions from image pixels ( Mnih et al. , 2013 ; Levine et al. , 2016 ; Nair et al. , 2018 ; Yan et al. , 2020 ; Andrychowicz et al. , 2020 ) . However , one significant ch... | This paper studies an important problem in vision-based RL: how to adapt a pre-trained policy to an unseen environment in a self-supervised manner. To do this, the authors introduce an auxiliary task branch that can be used to tune the intermediate representation of the policy network on the fly in a self-supervised ma... | SP:ee3f3ba07e22b31ba47ef01b1fc523bbbddddd5c |
Self-Supervised Policy Adaptation during Deployment | 1 INTRODUCTION . Deep reinforcement learning ( RL ) has achieved considerable success when combined with convolutional neural networks for deriving actions from image pixels ( Mnih et al. , 2013 ; Levine et al. , 2016 ; Nair et al. , 2018 ; Yan et al. , 2020 ; Andrychowicz et al. , 2020 ) . However , one significant ch... | The authors present a method for online policy adaptation during domain transfer in the case no reward is available in the target domain. They achieve this by adding an auxiliary self-supervised task, such as inverse dynamics prediction, that helps shape a set of features shared with the policy during training. At test... | SP:ee3f3ba07e22b31ba47ef01b1fc523bbbddddd5c |
A Neural Network MCMC sampler that maximizes Proposal Entropy | 1 INTRODUCTION . Sampling from unnormalized distributions is important for many applications , including statistics , simulations of physical systems , and machine learning . However , the inefficiency of state-of-the-art sampling methods remains a main bottleneck for many challenging applications , such as protein fol... | The paper argues that a better objective to train neural MCMC kernels is to maximize the proposal entropy (Titsias & Dellaportas, 2019) and demonstrate a method on doing so. The method shows improved sampling efficiency compared to previous method, especially one that optimize the alternative L2 expected jump. The nove... | SP:e6d26523e1b8f59840142193dcdd740ded86e9fa |
A Neural Network MCMC sampler that maximizes Proposal Entropy | 1 INTRODUCTION . Sampling from unnormalized distributions is important for many applications , including statistics , simulations of physical systems , and machine learning . However , the inefficiency of state-of-the-art sampling methods remains a main bottleneck for many challenging applications , such as protein fol... | This paper proposes a new MCMC transition kernel. This kernel is parameterized by neural networks and is optimized through an objective maximizing the proposal entropy. Specifically, the authors use a combination of a flow model and non-volume preserving flow in [Dinh et al., 2016] as the neural network parameterized k... | SP:e6d26523e1b8f59840142193dcdd740ded86e9fa |
Learning to Recombine and Resample Data For Compositional Generalization | 1 INTRODUCTION How can we build machine learning models with the ability to learn new concepts in context from little data ? Human language learners acquire new word meanings from a single exposure ( Carey & Bartlett , 1978 ) , and immediately incorporate words and their meanings productively and compositionally into l... | To tackle situations where compositionality is mostly required at inference time, the paper proposes a novel data augmentation method with an RNN based generator (recombination); to make the generator generate highly compositional patterns, the paper proposes a resampling method. The methods have been tested on two ben... | SP:0edafd92d3e0c42274852ec5c726e65cc79b0931 |
Learning to Recombine and Resample Data For Compositional Generalization | 1 INTRODUCTION How can we build machine learning models with the ability to learn new concepts in context from little data ? Human language learners acquire new word meanings from a single exposure ( Carey & Bartlett , 1978 ) , and immediately incorporate words and their meanings productively and compositionally into l... | This paper presents a prototype-based method for data augmentation based on a generative model without rule/template based requirements. The generative model creates new input-output pairs from training fragments (recombination: rewrite model conditioned on multiple examples), and samples in low-density places (rare wo... | SP:0edafd92d3e0c42274852ec5c726e65cc79b0931 |
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge | 1 INTRODUCTION . Causal inference models for estimating individualized treatment effects ( ITE ) are designed to provide actionable intelligence as part of decision support systems and , when deployed on mission-critical domains , such as healthcare , require safety and robustness above all ( Shalit et al. , 2017 ; Ala... | the paper attacks the problem of model selection for individual treatement effect (ITE) models when the domain of learning and prediction differ. Proposal is to use causal consistency as an additional "regularizer" in existing domain adaptation (DA) model selection methods. The "regularizer" would be scoring to which e... | SP:a877e36694e5ce6ac8fd441a765229a6574a3c16 |
Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge | 1 INTRODUCTION . Causal inference models for estimating individualized treatment effects ( ITE ) are designed to provide actionable intelligence as part of decision support systems and , when deployed on mission-critical domains , such as healthcare , require safety and robustness above all ( Shalit et al. , 2017 ; Ala... | This paper proposes a novel interventional causal model selection (ICMS) score to select individualized treatment effects (ITE) models under the unsupervised domain adaption (UDA) setting. The problem is fundamentally challenging as counterfactual outcomes cannot be observed. The authors make an assumption that the und... | SP:a877e36694e5ce6ac8fd441a765229a6574a3c16 |
Interactive Visualization for Debugging RL | 1 INTRODUCTION . Machine learning systems have made impressive advances due to their ability to learn high dimensional models from large amounts of data ( LeCun et al. , 2015 ) . However , high dimensional models are hard to understand and trust ( Doshi-Velez & Kim , 2017 ) . Many tools exist for addressing this challe... | The paper deals with debugging of black-box deep reinforcement learning (RL) agents to better understand and fix their policies. The authors propose diverse tools for, among others, visualizing the state space in terms of calculated statistics, analyzing the taken actions across learning episodes or exploring the repla... | SP:d8cd0216bc99e82a957d527a342bcefc5b69ec3c |
Interactive Visualization for Debugging RL | 1 INTRODUCTION . Machine learning systems have made impressive advances due to their ability to learn high dimensional models from large amounts of data ( LeCun et al. , 2015 ) . However , high dimensional models are hard to understand and trust ( Doshi-Velez & Kim , 2017 ) . Many tools exist for addressing this challe... | a. This article contributes a framework and tool for visualising data collected from a policy during RL training. It also contributes a set of views for visualising RL training data that could be used in general. Finally, the paper contributes a high-level workflow and a set of example use cases for how this tool mig... | SP:d8cd0216bc99e82a957d527a342bcefc5b69ec3c |
Center-wise Local Image Mixture For Contrastive Representation Learning | 1 INTRODUCTION . Learning general representations that can be transferable to different downstream tasks is a key challenge in computer vision . This is usually achieved by fully supervised learning paradigm , e.g. , making use of ImageNet labels for pretraining over the past several years . Recently , self-supervised ... | This paper focuses on contrastive learning for performing self-supervised network pre-training. Two components are proposed: First, to select semantically similar images that are pulled together in the contrastive learning, the paper proposes "center-wise local image mixture" (CLIM) - both k-means clustering and knn n... | SP:41f62bc0a7b3a9d4debaa2b1345538727a3f680e |
Center-wise Local Image Mixture For Contrastive Representation Learning | 1 INTRODUCTION . Learning general representations that can be transferable to different downstream tasks is a key challenge in computer vision . This is usually achieved by fully supervised learning paradigm , e.g. , making use of ImageNet labels for pretraining over the past several years . Recently , self-supervised ... | The paper addresses the problem of contrastive representation learning, and proposes a new data augmentation, dubbed CLIM, that leverages similarity between images. Instead of generating positives pairs using different transformation of the same image -as it is standard in contrastive learning-, positive pairs are gene... | SP:41f62bc0a7b3a9d4debaa2b1345538727a3f680e |
USING OBJECT-FOCUSED IMAGES AS AN IMAGE AUGMENTATION TECHNIQUE TO IMPROVE THE ACCURACY OF IMAGE-CLASSIFICATION MODELS WHEN VERY LIMITED DATA SETS ARE AVAILABLE | Today , many of the machine learning models are extremely data hungry . On the other hand , the accuracy of the algorithms used is very often affected by the amount of the training data available , which is , unfortunately , rarely abundant . Fortunately , image augmentation is one of the very powerful techniques that ... | Authors propose an augmentation technique for image classification. The augmented image is obtained by segmenting the salient object and masking the background. Therefore the technique gives one additional augmented image per each training image. The authors show an improved performance when using this augmentation on ... | SP:500d68ce2d8f1e6fea4fa54703101c6823ba2b6c |
USING OBJECT-FOCUSED IMAGES AS AN IMAGE AUGMENTATION TECHNIQUE TO IMPROVE THE ACCURACY OF IMAGE-CLASSIFICATION MODELS WHEN VERY LIMITED DATA SETS ARE AVAILABLE | Today , many of the machine learning models are extremely data hungry . On the other hand , the accuracy of the algorithms used is very often affected by the amount of the training data available , which is , unfortunately , rarely abundant . Fortunately , image augmentation is one of the very powerful techniques that ... | This work proposes to augment object focused image to improve image classification. Essentially, this work tries to remove background from original image using an existing algorithm and human editor, and train an image classification model using different combinations of original images and background-removed image. Fi... | SP:500d68ce2d8f1e6fea4fa54703101c6823ba2b6c |
Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning | Sampling-based neural architecture search ( NAS ) always guarantees better convergence yet suffers from huge computational resources compared with gradient-based approaches , due to the rollout bottleneck – exhaustive training for each sampled generation on proxy tasks . This work provides a general pipeline to acceler... | The paper propose a few improvements to the sampling-based NAS using RL: 1) an uncertainty-aware critic to decide whether the sample needs to be trained; 2) a life-long knowledge pool to initialize the sample that needs training; and 3) an architecture experience buffer to reuse old samples for RL training. The experim... | SP:5c6f72812c5b61731e649e7b37d31629dfd9a7ba |
Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning | Sampling-based neural architecture search ( NAS ) always guarantees better convergence yet suffers from huge computational resources compared with gradient-based approaches , due to the rollout bottleneck – exhaustive training for each sampled generation on proxy tasks . This work provides a general pipeline to acceler... | This paper proposes a fast general framework (FNAS) for neural architecture search (NAS) problem to enhance the processing efficiency up to 10x times. Three interesting strategies (UAC, LKP, AEB) for reinforcement learning (RL) processing are introduced in the proposed FNAS and evaluated by extensive experiments to sho... | SP:5c6f72812c5b61731e649e7b37d31629dfd9a7ba |
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices | 1 INTRODUCTION . A general-purpose agent should be able to perform multiple related tasks across multiple related environments . Our goal is to develop agents that can perform a variety of tasks in novel environments , based on previous experience and only a small amount of experience in the new environment . For examp... | This paper introduces DREAM, a meta-RL approach that decouples exploration from exploitation. An exploitation policy learns to maximize rewards that are conditioned on an encoder that learns task relevant information. Then an exploration policy learns to collect data that maximizes the mutual information between the en... | SP:29d472b7efdb02e3449a9edebeb54165b5f2becd |
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices | 1 INTRODUCTION . A general-purpose agent should be able to perform multiple related tasks across multiple related environments . Our goal is to develop agents that can perform a variety of tasks in novel environments , based on previous experience and only a small amount of experience in the new environment . For examp... | The paper investigates the exploration-exploitation problem in meta-learning. The authors explain the problem of coupled exploration and validate it through a toy example. To overcome this issue, the paper introduces DREAM, a meta-algorithm decoupling exploration and exploitation. In the first step, DREAM learns an exp... | SP:29d472b7efdb02e3449a9edebeb54165b5f2becd |
Graph Deformer Network | 1 INTRODUCTION . Graph is a flexible and universal data structure consisting of a set of nodes and edges , where node can represent any kind of objects and edge indicates some relationship between a pair of nodes . Research on graphs is not only important in theory , but also beneficial to in wide backgrounds of applic... | In order to perform anisotropic convolution on graphs, this paper proposes to project a local neighborhood into a unified virtual space by introducing anchor nodes. A theoretical analysis is provided to show the expressive power of the proposed graph deformer operation on graph isomorphism test. Extensive experiments a... | SP:80503d1fec17a71f526e1bf17459a7379f89383b |
Graph Deformer Network | 1 INTRODUCTION . Graph is a flexible and universal data structure consisting of a set of nodes and edges , where node can represent any kind of objects and edge indicates some relationship between a pair of nodes . Research on graphs is not only important in theory , but also beneficial to in wide backgrounds of applic... | This work proposes the Graph Deformer Network (GDN), whose key component is the proposed Graph Deformer Convolution (GDC). The GDC is based on the attention mechanism, where a fixed number of query vector comes from a clustering process of some randomly sampled nodes (In my opinion, the q vector in the paper should be ... | SP:80503d1fec17a71f526e1bf17459a7379f89383b |
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent | 1 INTRODUCTION . Federated learning refers to the collaborative training of a machine learning model across agents with distinct data sets , and it applies at different scales , from industrial data silos to mobile devices ( Kairouz et al. , 2019 ) . While some common challenges exist , such as the general statistical ... | This paper proposes distributed SVGD, which maintains N particles both on the server and on the client. The communication between the server and the client is conducted by uploading/downloading these N particles. The learning of local client is formulated as inferring corresponding tilted distribution. Experiments are ... | SP:81d2d5d9bfe2974415843ec016c72b80a761a20e |
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent | 1 INTRODUCTION . Federated learning refers to the collaborative training of a machine learning model across agents with distinct data sets , and it applies at different scales , from industrial data silos to mobile devices ( Kairouz et al. , 2019 ) . While some common challenges exist , such as the general statistical ... | This paper proposes a Bayesian optimization algorithm in the context of federated learning. The whole framework is built on top of generalized Bayesian learning. To overcome the locality of clients' distributions, the authors propose their solution as an integration of Partitioned Variational Inference (PVI) and Stein ... | SP:81d2d5d9bfe2974415843ec016c72b80a761a20e |
Neighbourhood Distillation: On the benefits of non end-to-end distillation | 1 INTRODUCTION . As Deep Neural Networks improve on challenging tasks , they also become deeper and bigger . Image classification convolutional neural networks grew from 5 layers in LeNet ( LeCun et al. , 1998 ) to more than a 100 in the latest ResNet models ( He et al. , 2016 ) . However , as models grow in size , tra... | This paper introduces Neighbourhood Distillation (ND), a new training pipeline for knowledge distillation (KD), which splits the student network into smaller neighbourhoods and trains them independently. The authors breaks away from the end-to-end paradigm in previous KD methods and provides empirical evidence to revea... | SP:be68300138280bab710e907dfc81395c16a270cf |
Neighbourhood Distillation: On the benefits of non end-to-end distillation | 1 INTRODUCTION . As Deep Neural Networks improve on challenging tasks , they also become deeper and bigger . Image classification convolutional neural networks grew from 5 layers in LeNet ( LeCun et al. , 1998 ) to more than a 100 in the latest ResNet models ( He et al. , 2016 ) . However , as models grow in size , tra... | This paper studies knowledge distillation in the context of parallelly training sub-networks (called neighbourhoods) instead of commonly used end-to-end training paradigm. The authors explore the applications of the proposed neighbourhoods distillation in improving sparse networks, searching a good student structure g... | SP:be68300138280bab710e907dfc81395c16a270cf |
Learning from Noisy Data with Robust Representation Learning | 1 INTRODUCTION . Data in real life is noisy . However , deep models with remarkable performance are mostly trained on clean datasets with high-quality human annotations . Manual data cleaning and labeling is an expensive process that is difficult to scale . On the other hand , there exists almost infinite amount of noi... | The authors of the paper propose to use the contrastive loss, the mixup prototypical loss, and a reconstruction loss to regularize the learned representation in order to achieve robustness under various kinds of noise like label noise, out-of-distribution input, and input corruption. A noise-cleaning process based on t... | SP:9699e0e908af5e3404df56498afe3d6c7333f431 |
Learning from Noisy Data with Robust Representation Learning | 1 INTRODUCTION . Data in real life is noisy . However , deep models with remarkable performance are mostly trained on clean datasets with high-quality human annotations . Manual data cleaning and labeling is an expensive process that is difficult to scale . On the other hand , there exists almost infinite amount of noi... | The paper proposes noise-robust contrastive learning to combat label noise, out-of-distribution input and input corruption simultaneously. In particular, this paper embeds images into low-dimensional representations by training an autoencoder, and regularizes the geometric structure of the representations by contrastiv... | SP:9699e0e908af5e3404df56498afe3d6c7333f431 |
Rethinking Soft Labels for Knowledge Distillation: A Bias–Variance Tradeoff Perspective | 1 INTRODUCTION . For deep neural networks ( Goodfellow et al. , 2016 ) , knowledge distillation ( KD ) ( Ba & Caruana , 2014 ; Hinton et al. , 2015 ) refers to the technique that uses well-trained networks to guide the training of another network . Typically , the well-trained network is named as the teacher network wh... | In this paper, the authors studied the soft labels for knowledge distillation from a bias-variance tradeoff perspective. Specifically, the authors first provide a mathematically descriptions of the bias-variance decomposition in knowledge distillation. Then, based on the theoretically analysis and experiments, the auth... | SP:7d26e683800476ec3617f4bdb759f690b3b7daed |
Rethinking Soft Labels for Knowledge Distillation: A Bias–Variance Tradeoff Perspective | 1 INTRODUCTION . For deep neural networks ( Goodfellow et al. , 2016 ) , knowledge distillation ( KD ) ( Ba & Caruana , 2014 ; Hinton et al. , 2015 ) refers to the technique that uses well-trained networks to guide the training of another network . Typically , the well-trained network is named as the teacher network wh... | The paper shows a new perspective of tackling the knowledge distillation problem. The author(s) have decomposed the expected student's training error into the bias, variance, and irreducible noise parts. This decomposition is further rewritten as two parts: one for bias reduction and another for variance reduction. The... | SP:7d26e683800476ec3617f4bdb759f690b3b7daed |
Neural Architecture Search without Training | 1 INTRODUCTION . The success of deep learning in computer vision is in no small part due to the insight and engineering efforts of human experts , allowing for the creation of powerful architectures for widespread adoption ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2015 ; He et al. , 2016 ; Szegedy et al. , 20... | The paper mainly introduces a metric to benchmark the performance of neural networks without training – the correlation of Jacobian subject to different augmented versions of a single image. The key motivation is, high-performance networks tend to represent data of small perturbations with different hyperplanes at init... | SP:142f883313e89c9f27904da7aa5e3e7063dffc4d |
Neural Architecture Search without Training | 1 INTRODUCTION . The success of deep learning in computer vision is in no small part due to the insight and engineering efforts of human experts , allowing for the creation of powerful architectures for widespread adoption ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2015 ; He et al. , 2016 ; Szegedy et al. , 20... | This paper attempts to infer a network's accuracy at initialization without training it, which can speed up neural architecture search and greatly reduce the search cost. Specifically, they propose a metric based on the Jacobian of the loss with respect to a minibatch of input data. The authors show that with this metr... | SP:142f883313e89c9f27904da7aa5e3e7063dffc4d |
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics | 1 INTRODUCTION . In typical statistical machine learning algorithms , test data are assumed to stem from the same distribution as training data ( Hastie et al. , 2009 ) . However , this assumption is often violated in practical situations , and the trained model results in unexpectedly poor performance ( QuioneroCandel... | This paper proposes a domain adaptation technique when source data is not available. The exponentially weighted average of BN statistics from source training along with the trained model is utilized to align source and target distributions. Source model is divided into feature encoder and classifier components based on... | SP:b91c4d5f9cde00d87fd76c1eb710322a767af87d |
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics | 1 INTRODUCTION . In typical statistical machine learning algorithms , test data are assumed to stem from the same distribution as training data ( Hastie et al. , 2009 ) . However , this assumption is often violated in practical situations , and the trained model results in unexpectedly poor performance ( QuioneroCandel... | In the work, the authors focus on tackling the problem of source free domain adaptation. The proposed method mainly has two parts, in which the second is nearly the same as the SHOT-IM as in Liang et al., 2020 [1], while the first part aims at coping with this problem from a new perspective to align the distribution of... | SP:b91c4d5f9cde00d87fd76c1eb710322a767af87d |
Learning What Not to Model: Gaussian Process Regression with Negative Constraints | We empirically demonstrate that our GP-NC framework performs better than the traditional GP learning and that our framework does not affect the scalability of Gaussian Process regression and helps the model converge faster as the size of the data increases . Gaussian Process ( GP ) regression fits a curve on a set of d... | This paper incorporates information of obstacles to avoid (e.g robot navigation trajectory in the room where the robot has to avoid items such as furniture) into Gaussian process regression fit. They call the obstacles, negative datapairs and the rest of data, positive datapairs. The aim is to have a GP where the proba... | SP:7003fdde96baedb55a47e5b42ad8d1866a86f5c7 |
Learning What Not to Model: Gaussian Process Regression with Negative Constraints | We empirically demonstrate that our GP-NC framework performs better than the traditional GP learning and that our framework does not affect the scalability of Gaussian Process regression and helps the model converge faster as the size of the data increases . Gaussian Process ( GP ) regression fits a curve on a set of d... | This paper in concerned with Gaussian process regression under constraints that aim to discourage the model from learning certain values (negative constraints). These are called negative data pairs, and the authors propose an extension to the standard GP methodology to incorporate these constraints in the model. This i... | SP:7003fdde96baedb55a47e5b42ad8d1866a86f5c7 |
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction | 1 INTRODUCTION . In the era of Internet-of-things ( IoT ) data explosion ( Biookaghazadeh et al. , 2018 ) , efficient information acquisition and on-sensor data dimensionality reduction techniques are in great need . Compressive sensing is the state-of-the-art signal sensing technique that is applicable to on-sensor da... | The paper proposes a framework for jointly optimizing a selective sensing operator and a neural network for reconstruction. The motivation is to alleviate the quadratic (in dimensions) complexity associated with standard compressive sensing by using a dimension-free selective sensing approach, which can be jointly opti... | SP:0c846c2c569a61b5677f5852a5c6bcfba1944d51 |
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction | 1 INTRODUCTION . In the era of Internet-of-things ( IoT ) data explosion ( Biookaghazadeh et al. , 2018 ) , efficient information acquisition and on-sensor data dimensionality reduction techniques are in great need . Compressive sensing is the state-of-the-art signal sensing technique that is applicable to on-sensor da... | The paper proposes a nonuniform sampling design scheme chosen using training data to reduce the computation of compressed sensing acquisition. The use of learning methods such as back propagation for the nonuniform sampling design problem is interesting. However, compressive sensing approaches in practice do not perfor... | SP:0c846c2c569a61b5677f5852a5c6bcfba1944d51 |
Deep Ensemble Kernel Learning | 1 INTRODUCTION . In recent years , there has been a growing interest in Bayesian deep learning ( DL ) , where the point predictions of traditional deep neural network ( DNN ) models are replaced with full predictive distributions using Bayes ’ Rule ( Neal , 2012 ; Wilson , 2020 ) . The advantages of Bayesian DL over tr... | The authors introduce a deep ensemble kernel learning approach as a linear-based learning combination, from a deep learning scheme, to approximate kernel functions under a Bayesian (GP) framework. Namely, a universal kernel approximation strategy is proposed from eigen-based decomposition and deep learning-based functi... | SP:5fff81a3906d13d4a4105e509b399c203d8e1d58 |
Deep Ensemble Kernel Learning | 1 INTRODUCTION . In recent years , there has been a growing interest in Bayesian deep learning ( DL ) , where the point predictions of traditional deep neural network ( DNN ) models are replaced with full predictive distributions using Bayes ’ Rule ( Neal , 2012 ; Wilson , 2020 ) . The advantages of Bayesian DL over tr... | This paper proposes a variant of the Deep Kernel Learning model (DKL) [1] where multiple independent networks are trained for the features instead of a single network. In addition, the paper proposes to use a linear kernel as a base kernel which allows for universal approximation of any arbitrary kernel, as well as all... | SP:5fff81a3906d13d4a4105e509b399c203d8e1d58 |
SAFENet: A Secure, Accurate and Fast Neural Network Inference | 1 INTRODUCTION . Neural network inference as a service ( NNaaS ) is an effective method for users to acquire various intelligent services from powerful servers . NNaaS includes many emerging , intelligent , client-server applications such as smart speakers , voice assistants , and image classifications Mishra et al . (... | The main contribution of this paper is a new heuristic for identifying "less useful" activation channels. The authors then propose using simple approximations for activation functions for these channels without compromising network accuracy. The main novelty in the approximation used by the authors is flexibility in th... | SP:476e903197a3f3861692dfaa7136c5a274414e73 |
SAFENet: A Secure, Accurate and Fast Neural Network Inference | 1 INTRODUCTION . Neural network inference as a service ( NNaaS ) is an effective method for users to acquire various intelligent services from powerful servers . NNaaS includes many emerging , intelligent , client-server applications such as smart speakers , voice assistants , and image classifications Mishra et al . (... | The paper present a system for two-party deep learning inference. The main contribution is activation layers that are more expensive in two-party computation are replaced by approximations dynamically based on the training data. To this end, the authors use a divide-and-conquer approach to gauge the impact of replacing... | SP:476e903197a3f3861692dfaa7136c5a274414e73 |
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints | We develop a new framework for learning variational autoencoders and other deep generative models that balances generative and discriminative goals . Our framework optimizes model parameters to maximize a variational lower bound on the likelihood of observed data , subject to a task-specific prediction constraint that ... | The paper proposes a framework for semi-supervised settings to leverage both unlabeled data and (limited) labeled data where VAEs are trained subject to regularization terms from label information. More specifically, the proposed method trains a VAE and a NN classifier simultaneously by optimizing an objective that con... | SP:3761ec50c1dd06a108e7dc1a6b56b205b61d00c0 |
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints | We develop a new framework for learning variational autoencoders and other deep generative models that balances generative and discriminative goals . Our framework optimizes model parameters to maximize a variational lower bound on the likelihood of observed data , subject to a task-specific prediction constraint that ... | This paper proposes a new VAE framework for semi-supervised problems, which uses the latent representation \\(z\\) to reconstruct input image \\(x\\) and to serve as the features for the classification of the label of \\(x\\). Based on this framework, the paper also proposes additional "cycle" losses, where the label p... | SP:3761ec50c1dd06a108e7dc1a6b56b205b61d00c0 |
On Dynamic Noise Influence in Differential Private Learning | 1 INTRODUCTION . In the era of big data , privacy protection in machine learning systems is becoming a crucial topic as increasing personal data involved in training models ( Dwork et al. , 2020 ) and the presence of malicious attackers ( Shokri et al. , 2017 ; Fredrikson et al. , 2015 ) . In response to the growing de... | Gradient Descent and related variants are the defacto standard algorithms for optimizing empirical risk functions. Since published models have been shown in the literature to leak private information, the problem of performing gradient descent under privacy constraints is an important one. Given a fixed privacy budget ... | SP:102f337fcdb0455ef7da2fe20f8684cb61a54314 |
On Dynamic Noise Influence in Differential Private Learning | 1 INTRODUCTION . In the era of big data , privacy protection in machine learning systems is becoming a crucial topic as increasing personal data involved in training models ( Dwork et al. , 2020 ) and the presence of malicious attackers ( Shokri et al. , 2017 ; Fredrikson et al. , 2015 ) . In response to the growing de... | The paper studies private gradient descent when the noise added to each of the iteration is dynamically scheduled. Prior to this work, the work of Zhou et al. tries to achieve the same for DP-SGD and they analyze their algorithm for many variants of adaptive gradient descent based method. The difference with Zhou et al... | SP:102f337fcdb0455ef7da2fe20f8684cb61a54314 |
Ricci-GNN: Defending Against Structural Attacks Through a Geometric Approach | Graph neural networks ( GNNs ) rely heavily on the underlying graph topology and thus can be vulnerable to malicious attacks targeting at perturbing graph structures . We propose a novel GNN defense algorithm against such attacks . In particular , we use a robust representation of the input graph based on the theory of... | In Ricci-GCN new graphs are resampled in each iteration of the training phase based on the Ricci flow metric. The Ricci flow incorporates curvature information and captures the intrinsic geometry of the graph. Compared to e.g. spectral embedding it is more robust to structural perturbations. This leads to improved robu... | SP:bacf7f05516ec99a3dafaedb8cba0f0b2831f99c |
Ricci-GNN: Defending Against Structural Attacks Through a Geometric Approach | Graph neural networks ( GNNs ) rely heavily on the underlying graph topology and thus can be vulnerable to malicious attacks targeting at perturbing graph structures . We propose a novel GNN defense algorithm against such attacks . In particular , we use a robust representation of the input graph based on the theory of... | The paper proses a new adversarial (poisoning) defense based on a known graph reweighting scheme known as the ricci curvature. The ricci curvature assigns a weight to each edge that captures the graph structure, i.e. the value reflects whether the edge is an inter-community connection or an intracommunity connection. E... | SP:bacf7f05516ec99a3dafaedb8cba0f0b2831f99c |
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning | 1 INTRODUCTION . Cooperative multi-agent reinforcement learning ( MARL ) aims to instill in agents policies that maximize the team reward accumulated over time ( Panait & Luke ( 2005 ) ; Busoniu et al . ( 2008 ) ; Tuyls & Weiss ( 2012 ) ) , which has great potential to address complex real-world problems , such as coor... | This paper proposes a novel MARL framework named FSV, which incorporates the idea of energy-based policies and an efficient linear decomposition architecture in the joint action-value function with multi-agent maximum entropy reinforcement learning. Besides, the authors propose the IGO, which extends the IGM in stochas... | SP:f643363fb9654443375b1772cd88b53dbe1bed87 |
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning | 1 INTRODUCTION . Cooperative multi-agent reinforcement learning ( MARL ) aims to instill in agents policies that maximize the team reward accumulated over time ( Panait & Luke ( 2005 ) ; Busoniu et al . ( 2008 ) ; Tuyls & Weiss ( 2012 ) ) , which has great potential to address complex real-world problems , such as coor... | The paper proposes a Q-factorization method by assuming an energy-based policies model. Q-functions are formulated as soft value functions with the energy parameters, and this adoption renders the function factorization more flexible compared to existing ones. The proposed solution applies to continuous-action tasks, a... | SP:f643363fb9654443375b1772cd88b53dbe1bed87 |
Generalization in data-driven models of primary visual cortex | 1 INTRODUCTION . A long lasting challenge in sensory neuroscience is to understand the computations of neurons in the visual system stimulated by natural images ( Carandini et al. , 2005 ) . Important milestones towards this goal are general system identification models that can predict the response of large population... | The authors adopt a data-driven approach to neural system identification. They train a neural network consisting of a "core" and a "readout" in an end-to-end fashion to learn stimulus (visual inputs) -- response (single neuron activity) pairs. Since the core is shared across neurons, these stimulus-response pairs can b... | SP:06fe119d437e7f517496d554a091979ff74c9431 |
Generalization in data-driven models of primary visual cortex | 1 INTRODUCTION . A long lasting challenge in sensory neuroscience is to understand the computations of neurons in the visual system stimulated by natural images ( Carandini et al. , 2005 ) . Important milestones towards this goal are general system identification models that can predict the response of large population... | The authors train a neural net to predict responses of mouse V1 L2/3 neurons to visual stimulation. The NN has a "core" that is shared between all neurons, and a neuron-specific readout. They train the core on multiple animals and find that it can generalize well: it can be used in a new animal and (with sufficient tra... | SP:06fe119d437e7f517496d554a091979ff74c9431 |
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift | 1 Introduction . Some applications of deep learning rely only on point estimate predictions made by a neural network . However , many critical applications also require reliable predictive uncertainty estimates and robustness under the presence of dataset shift , that is , when the observed data distribution at deploym... | The paper suggests a new approach to the construction of ensembles of deep neural networks (DNN). Unlike previous methods which usually deal with multiple DNNs of same structure authors propose to form an ensemble of networks with different architecture. The main claim is that using diverse architectures increases dive... | SP:71bc23f11137956757268354ed02ac3799373323 |
Neural Ensemble Search for Uncertainty Estimation and Dataset Shift | 1 Introduction . Some applications of deep learning rely only on point estimate predictions made by a neural network . However , many critical applications also require reliable predictive uncertainty estimates and robustness under the presence of dataset shift , that is , when the observed data distribution at deploym... | The paper explores whether one can use Architecture Search to enhance ensemble diversity. They start with the observation that embeddings generated by different architectures (for multiple different initialization per architecture) are well separated from each other. They then try out a couple of architecture search me... | SP:71bc23f11137956757268354ed02ac3799373323 |
DO-GAN: A Double Oracle Framework for Generative Adversarial Networks | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) have been applied in various domains such as image and video generation , image-to-image translation and text-to-image synthesis ( Liu et al. , 2017 ; Reed et al. , 2016 ) . Various architectures are proposed to generate more realist... | This paper applies Double-Oracle (DO) / PSRO to training a GAN, a 2-player zero-sum game. DO cannot be applied directly "out-of-the-box". Instead of an exact oracle, the generator and discriminator are trained using local gradient optimizers for a finite number of steps. Also, in DO, the meta-game matrix can grow very ... | SP:266023140bbd3039a0bc65c2f59f3edcf34ed58b |
DO-GAN: A Double Oracle Framework for Generative Adversarial Networks | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) have been applied in various domains such as image and video generation , image-to-image translation and text-to-image synthesis ( Liu et al. , 2017 ; Reed et al. , 2016 ) . Various architectures are proposed to generate more realist... | This paper proposes to use the well-known Double Oracle methods for solving large scale games for computing the equilibrium in GANs. The main idea of a mixed strategy being a mixture over generators (and mixture over discriminator for toher player( is from Hsieh et al. The double oracle approach is shown to yield super... | SP:266023140bbd3039a0bc65c2f59f3edcf34ed58b |
Scalable Graph Neural Networks for Heterogeneous Graphs | 1 INTRODUCTION . In recent years , deep learning on graphs has attracted a great deal of interest , with new applications ranging from social networks and recommender systems , to biomedicine , scene understanding , and modeling of physics ( Wu et al. , 2020 ) . One popular branch of graph learning is based on the idea... | This paper aims to propose a new GNN for heterogeneous graphs, which is scalable to large-scale graphs. The proposed idea is to leverage an existing model called SIGN, which simplifies GCN by dropping the non-linear transformation from intermediate layers, and extend it to heterogeneous graphs. The results on several b... | SP:78faeffc7a6d60225bded8a9e6eee2aa369138fc |
Scalable Graph Neural Networks for Heterogeneous Graphs | 1 INTRODUCTION . In recent years , deep learning on graphs has attracted a great deal of interest , with new applications ranging from social networks and recommender systems , to biomedicine , scene understanding , and modeling of physics ( Wu et al. , 2020 ) . One popular branch of graph learning is based on the idea... | The authors propose a method to broaden the scope of SIGN, a technique recently introduced for single-relational graphs. The method allows SIGN to also be applied to multi-relational graphs (often called heterogeneous or knowledge graphs in different communities). In SIGN, various powers of the Laplacian are precompute... | SP:78faeffc7a6d60225bded8a9e6eee2aa369138fc |
CPR: Classifier-Projection Regularization for Continual Learning | 1 INTRODUCTION . Catastrophic forgetting ( McCloskey & Cohen , 1989 ) is a central challenge in continual learning ( CL ) : when training a model on a new task , there may be a loss of performance ( e.g. , decrease in accuracy ) when applying the updated model to previous tasks . At the heart of catastrophic forgetting... | The paper proposes to add a KL-divergence regularization to the objective of regularized continual learning in order to encourage the output prediction to be close to a uniform distribution over classes (i.e., increasing the entropy). They argue that this regularization makes the local minima flat and thus less prone t... | SP:8b679a434b4b83a626a6dafc1891068800c737a5 |
CPR: Classifier-Projection Regularization for Continual Learning | 1 INTRODUCTION . Catastrophic forgetting ( McCloskey & Cohen , 1989 ) is a central challenge in continual learning ( CL ) : when training a model on a new task , there may be a loss of performance ( e.g. , decrease in accuracy ) when applying the updated model to previous tasks . At the heart of catastrophic forgetting... | The authors argue that achieving wide local minima during the training of tasks, is beneficial for continual learning. The plausible intuition (explained in Fig. 1) is that it is easier to find a parameter setting that is beneficial for all tasks when tasks have wide local minima. They enforce wide local minima by addi... | SP:8b679a434b4b83a626a6dafc1891068800c737a5 |
Stochastic Proximal Point Algorithm for Large-scale Nonconvex Optimization: Convergence, Implementation, and Application to Neural Networks | 1 INTRODUCTION . Algorithm design for large-scale machine learning problems have been dominated by the stochastic ( sub ) gradient descent ( SGD ) and its variants ( Bottou et al. , 2018 ) . The main reasons are two-fold : on the one hand , the size of the data set may be so large that obtaining the full gradient infor... | In this paper the authors study stochastic proximal point algorithm for nonconvex optimization, where the model is iteratively updated by solving a proximal optimization problem based on a randomly selected loss function. The authors develop efficient implementation for solving the proximal optimization problem: first ... | SP:ff9b59f83d1d206ef246db96f13b43ac39c54db8 |
Stochastic Proximal Point Algorithm for Large-scale Nonconvex Optimization: Convergence, Implementation, and Application to Neural Networks | 1 INTRODUCTION . Algorithm design for large-scale machine learning problems have been dominated by the stochastic ( sub ) gradient descent ( SGD ) and its variants ( Bottou et al. , 2018 ) . The main reasons are two-fold : on the one hand , the size of the data set may be so large that obtaining the full gradient infor... | This paper studies the stochastic proximal point algorithm (SPPA) for large-scale nonconvex optimization problems. The authors propose to use Gauss-Newton to perform the proximal update in nonlinear least squares and L-BFGS or accelerated gradient for generic problems. The authors derive the convergence of SPPA to a st... | SP:ff9b59f83d1d206ef246db96f13b43ac39c54db8 |
Explainability for fair machine learning | 1 INTRODUCTION . Machine learning has repeatedly demonstrated astonishing predictive power due to its capacity to learn complex relationships from data . However , it is well known that machine learning models risk perpetuating or even exacerbating unfair biases learnt from historical data ( Barocas & Selbst , 2016 ; B... | The goal of the paper is to design mechanisms to explain the unfairness in the outcomes of a ML model and propose methods to mitigate unfairness. The paper uses the Shapley value framework. The main idea is to alter the prediction function so that instead of providing the classification score, an "unfairness" score is ... | SP:afb7cc467235d77ddcfc6b8745fa6096223d8fdd |
Explainability for fair machine learning | 1 INTRODUCTION . Machine learning has repeatedly demonstrated astonishing predictive power due to its capacity to learn complex relationships from data . However , it is well known that machine learning models risk perpetuating or even exacerbating unfair biases learnt from historical data ( Barocas & Selbst , 2016 ; B... | This paper presents a method for feature attribution for fairness of the classifier. They also demonstrate a feature augmentation technique to mitigate unfairness. They connect their attribution method to to the augmentation technique and demonstrate that their method can attribute the necessary changes to achieve fa... | SP:afb7cc467235d77ddcfc6b8745fa6096223d8fdd |
Decentralized Attribution of Generative Models | 1 INTRODUCTION Recent advances in generative models ( Goodfellow et al. , 2014 ) have enabled the creation of synthetic contents that are indistinguishable even by naked eyes ( Pathak et al. , 2016 ; Zhu et al. , 2017 ; Zhang et al. , 2017 ; Karras et al. , 2017 ; Wang et al. , 2018 ; Brock et al. , 2018 ; Miyato et al... | Fake content produced by generative models is of great concerns. This paper investigates attribution techniques to identify models that generated the content. The key theoretic result is the derivation of the sufficient conditions for decentralized attribution and the design of keys following these conditions. Thee pap... | SP:8b44a01fccccbcbe0b91b819c1525b30693a7bd8 |
Decentralized Attribution of Generative Models | 1 INTRODUCTION Recent advances in generative models ( Goodfellow et al. , 2014 ) have enabled the creation of synthetic contents that are indistinguishable even by naked eyes ( Pathak et al. , 2016 ; Zhu et al. , 2017 ; Zhang et al. , 2017 ; Karras et al. , 2017 ; Wang et al. , 2018 ; Brock et al. , 2018 ; Miyato et al... | This paper proposes a decentralized attribution to the generative model trained on the same dataset. The goal is to distinguish the user-end generative models, and thus facilitates the IP-protection. The idea is to use orthogonal keys to distinguish the generated samples from authentic data. Furthermore, this paper pro... | SP:8b44a01fccccbcbe0b91b819c1525b30693a7bd8 |
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning | 1 INTRODUCTION . Unsupervised learning is one of the most fundamental and challenging problems in machine learning , due to the absence of target labels to guide the learning process . Thanks to the enormous research efforts , there now exist many unsupervised learning methods that have shown promising results on real-... | The submission proposes an algorithm for the semi-supervised meta-learning (unsupervised meta-training + supervised meta-testing) setting of [1], which adapts the few-shot learning + evaluation setting of [2, 3] by omitting classification labels at meta-training time. The algorithm makes use of a variational auto-encod... | SP:e3ce73327452f27aa256253ba6b402635697820c |
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning | 1 INTRODUCTION . Unsupervised learning is one of the most fundamental and challenging problems in machine learning , due to the absence of target labels to guide the learning process . Thanks to the enormous research efforts , there now exist many unsupervised learning methods that have shown promising results on real-... | This paper proposes a method for unsupervised meta-learning based on using a variational autoencoder (VAE). The variational autoencoder model they use differs from the typical one in that it considers episode-specific datasets, where the approximate posterior can be computed as a function of the set (using transformer ... | SP:e3ce73327452f27aa256253ba6b402635697820c |
Flatness is a False Friend | 1 Introduction . Deep Neural Networks ( DNNs ) , with more parameters than data-points , trained with many passes of the same data , still manage to perform exceptionally on test data . The reasons for this remain laregly unsolved ( Neyshabur et al. , 2017 ) . However , DNNs are not completely immune to the classical p... | The authors of this article experimentally investigate whether flatness of the loss surface can be a good measure for generalization capabilities of neural networks. They present theoretical reasoning that suggests that weight regularization can lead to sharper local minima and better generalization although it is expe... | SP:182b04893072dc8b62cf379b19fb8fdec105b516 |
Flatness is a False Friend | 1 Introduction . Deep Neural Networks ( DNNs ) , with more parameters than data-points , trained with many passes of the same data , still manage to perform exceptionally on test data . The reasons for this remain laregly unsolved ( Neyshabur et al. , 2017 ) . However , DNNs are not completely immune to the classical p... | This paper argues that as the cross-entropy loss goes to zero, since the correct logit increases in magnitude the entries of the Hessian diminish to zero. Such overfitting on the training set and a small spectral norm of the Hessian should result in poor generalization error. Motivated by this, the paper experimentally... | SP:182b04893072dc8b62cf379b19fb8fdec105b516 |
Interpreting Knowledge Graph Relation Representation from Word Embeddings | 1 INTRODUCTION . Knowledge graphs are large repositories of binary relations between words ( or entities ) in the form of ( subject , relation , object ) triples . Many models for representing entities and relations have been developed , so that known facts can be recalled and previously unknown facts can be inferred ,... | Recent works toward the understanding of word embeddings can explain how semantic word relationships, such as similarity, analogy and paraphrasing are encoded as low-rank projections of high dimensional vectors of co-occurrence statistics (Allen et al., 2019). Thus, the semantic relationships correspond to linear relat... | SP:e8329108f4d0fb74d9347dcb06c7fe6aff604ba9 |
Interpreting Knowledge Graph Relation Representation from Word Embeddings | 1 INTRODUCTION . Knowledge graphs are large repositories of binary relations between words ( or entities ) in the form of ( subject , relation , object ) triples . Many models for representing entities and relations have been developed , so that known facts can be recalled and previously unknown facts can be inferred ,... | Based on PMI word embedding, the authors categorize the knowledge graph relations into three types, which serve as the foundation of knowledge analysis. This paper is not well-motived but presents the methodology, well. However, nothing in this paper surprised me, because this seems like a ````''regular'' research in t... | SP:e8329108f4d0fb74d9347dcb06c7fe6aff604ba9 |
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation | 1 INTRODUCTION . Risk is one of the most important considerations in decision making , so should it be in reinforcement learning ( RL ) . As a prominent paradigm in RL that performs learning while accounting for risk , risksensitive RL explicitly models risk of decisions via certain risk measures and optimizes for rewa... | The paper studies risk-sensitive reinforcement learning with the entropic risk measure and function approximation. A meta algorithm based on value iteration is first proposed, then the paper proposes two concrete instantiations, one for linear function approximation and one for general function approximation. Regret bo... | SP:993930791c2d4699190c699e147ecc0518a4c6b4 |
Entropic Risk-Sensitive Reinforcement Learning: A Meta Regret Framework with Function Approximation | 1 INTRODUCTION . Risk is one of the most important considerations in decision making , so should it be in reinforcement learning ( RL ) . As a prominent paradigm in RL that performs learning while accounting for risk , risksensitive RL explicitly models risk of decisions via certain risk measures and optimizes for rewa... | This paper proposes a risk-sensitive algorithm with function approximation in reinforcement learning. To handle the uncertainty, the proposed algorithms consider an entropic risk value function controlled by a risk parameter, which provides a unified framework for both risk-sensitive and risk-averse settings. The main ... | SP:993930791c2d4699190c699e147ecc0518a4c6b4 |
Learning Predictive Communication by Imagination in Networked System Control | 1 INTRODUCTION . Networked system control ( NSC ) is extensively studied and widely applied , including connected vehicle control ( Jin & Orosz , 2014 ) , traffic signal control ( Chu et al. , 2020b ) , distributed sensing ( Xu et al. , 2016 ) , networked storage operation ( Qin et al. , 2015 ) etc . In NSC , agents ar... | The paper proposes to communicate predicted local states between neighboring agents to address the problem of delayed information in networked multi-agent reinforcement learning. To enable agents to predict future states, a world model is learned at each agent. It is empirically demonstrated that the proposed method ha... | SP:515995dd42b4aecbd625206b16aeaca43c5a1495 |
Learning Predictive Communication by Imagination in Networked System Control | 1 INTRODUCTION . Networked system control ( NSC ) is extensively studied and widely applied , including connected vehicle control ( Jin & Orosz , 2014 ) , traffic signal control ( Chu et al. , 2020b ) , distributed sensing ( Xu et al. , 2016 ) , networked storage operation ( Qin et al. , 2015 ) etc . In NSC , agents ar... | The paper provides an interesting way to add structure to MARL problems that have delay in the communication of state information. By explicitly building a predictive module for the future latent state of the agent and including that predicted state in the passed messages, it is possible that the agent will appropriate... | SP:515995dd42b4aecbd625206b16aeaca43c5a1495 |
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis | 1 INTRODUCTION . Given a never-before-seen object ( e.g. , a gadwall in Figure 1 ) , humans are able to generalize even from a single image of this object in different ways , including recognizing new object instances and imagining what the object would look like from different viewpoints . Achieving similar levels of ... | This paper proposes a "feedback-based bowtie network" FBNet for joint generative synthesis via a GAN-based framework (specifically HoloGAN) and few-shot fine-grained recognition. The key idea of this work is to supervise both networks jointly via feedback mechanisms between the two, which helps to improve both tasks: i... | SP:627a0f2c3be51ea6d1e8f56c7b2dd35142758509 |
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis | 1 INTRODUCTION . Given a never-before-seen object ( e.g. , a gadwall in Figure 1 ) , humans are able to generalize even from a single image of this object in different ways , including recognizing new object instances and imagining what the object would look like from different viewpoints . Achieving similar levels of ... | This paper presents a new dual-task of joint few-shot recognition and novel synthesis. The main idea of this paper is to learn a shared generative model across the dual-task to boost the performances of both tasks. To achieve this, bowtie networks are employed to jointly learn geometric and semantic representations wit... | SP:627a0f2c3be51ea6d1e8f56c7b2dd35142758509 |
Learning a Transferable Scheduling Policy for Various Vehicle Routing Problems based on Graph-centric Representation Learning | 1 INTRODUCTION . The Vehicle Routing Problem ( VRP ) , a well-known NP-hard problem , has been enormously studied since it appeared by Dantzig & Ramser ( 1959 ) . There have been numerous attempts to compute the exact ( optimal ) or approximate solutions for various types of vehicle routing problems by using mixed inte... | The paper presents a reinforcement learning approach to learn a routing policy for a family of Vehicle Routing Problems (VRPs). More precisely, the authors train a model for the min-max capacitated multi vehicle routing problem (mCVRP), then use it to solve variants of the problem that correspond to various VRP problem... | SP:c98108a3d1120eb4c9c34ba2e07545e9a3f93bdf |
Learning a Transferable Scheduling Policy for Various Vehicle Routing Problems based on Graph-centric Representation Learning | 1 INTRODUCTION . The Vehicle Routing Problem ( VRP ) , a well-known NP-hard problem , has been enormously studied since it appeared by Dantzig & Ramser ( 1959 ) . There have been numerous attempts to compute the exact ( optimal ) or approximate solutions for various types of vehicle routing problems by using mixed inte... | This paper considers the problem of capacitated vehicle routing which is a famous combinatorial optimization problem that is known to be NP-hard. This paper takes the approach of solving instances of this problem using RL. The goal is this problem is to minimize the maximum time (or makespan objective) for multiple veh... | SP:c98108a3d1120eb4c9c34ba2e07545e9a3f93bdf |
On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks | We examine Dropout through the perspective of interactions . Given N variables , there are O ( N2 ) possible pairwise interactions , O ( N3 ) possible 3-way interactions , i.e . O ( Nk ) possible interactions of k variables . Conversely , the probability of an interaction of k variables surviving Dropout at rate p is O... | The authors are analyzing to which extent dropout is regularizing the training stage of deep networks, showing that high-order interactions are discouraged, this being a proxy for a better generalization capability once spurious co-adaptations are removed. In an extended mathematical analysis, the authors carry out the... | SP:731300fd76e291c578ca23406efd2d149fb30df0 |
On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks | We examine Dropout through the perspective of interactions . Given N variables , there are O ( N2 ) possible pairwise interactions , O ( N3 ) possible 3-way interactions , i.e . O ( Nk ) possible interactions of k variables . Conversely , the probability of an interaction of k variables surviving Dropout at rate p is O... | This paper analyzes Dropout through the lens of k-way interactions. The central claim of this paper is that Dropout reduces interaction effects. This is shown through both theory and experiment. The theory suggests that a higher dropout rate reduces the effective learning speed of higher-order interactions. Experiments... | SP:731300fd76e291c578ca23406efd2d149fb30df0 |
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning | 1 INTRODUCTION . In semi-supervised learning ( SSL ) , a mapping is learned that predicts labels y for data points x from a dataset of labelled pairs ( xl , yl ) and unlabelled xu . SSL is of practical importance since unlabelled data are often cheaper to acquire and/or more abundant than labelled data . For unlabelled... | The authors introduce a discriminative model for semi-supervised learning for which several existing methods are special cases. In their model, for each data value, there is a distribution from which the label is sampled. Although this distribution is unknown, in their framework the sampling distribution's parameters a... | SP:58b222745ef2775a8925397ba2a98ba086e945e4 |
A Probabilistic Model for Discriminative and Neuro-Symbolic Semi-Supervised Learning | 1 INTRODUCTION . In semi-supervised learning ( SSL ) , a mapping is learned that predicts labels y for data points x from a dataset of labelled pairs ( xl , yl ) and unlabelled xu . SSL is of practical importance since unlabelled data are often cheaper to acquire and/or more abundant than labelled data . For unlabelled... | This paper proposes a probabilistic model to describe semi-supervised/unsupervised learning, which is further applied to model neuro-symbolic learning. Comparing to traditional unsupervised/semi-supervised learning formulations, the proposed model imposes a prior on the label distribution instead of input features. Whe... | SP:58b222745ef2775a8925397ba2a98ba086e945e4 |
Average Reward Reinforcement Learning with Monotonic Policy Improvement | 1 INTRODUCTION . The goal of Reinforcement Learning ( RL ) is to build agents that can learn high-performing behaviors through trial-and-error interactions with the environment . Broadly speaking , modern RL tackles two kinds of problems : episodic tasks and continuing tasks . In episodic tasks , the agent-environment ... | The paper proposes an extension of the performance improvement bound, introduced for the first time by Kakade & Langford (2002), to the case of average reward performance index instead of discounted return. The paper starts with a theoretical contribution in which all the steps of the original derivation are adapted fo... | SP:1510815ddfb253f977b3ce9b53ea02b4044ffb90 |
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