paper_name stringlengths 11 170 | text stringlengths 8.07k 307k | summary stringlengths 152 6.16k | paper_id stringlengths 43 43 |
|---|---|---|---|
Improving Zero-Shot Voice Style Transfer via Disentangled Representation Learning | 1 INTRODUCTION . Style transfer , which automatically converts a data instance into a target style , while preserving its content information , has attracted considerable attention in various machine learning domains , including computer vision ( Gatys et al. , 2016 ; Luan et al. , 2017 ; Huang & Belongie , 2017 ) , vi... | This submission proposes a training approach for voice style transfer using encoder-decoder framework and content and style representations. The approach combines multiple mutual-information (MI) based terms into a single objective function. One of the MI based terms is the MI between content and style representations.... | SP:86d37b08b4c0ab21d139c57bbe3b9e5535eeb3f9 |
Learning Lagrangian Fluid Dynamics with Graph Neural Networks | 1 INTRODUCTION . For many science and engineering problems , fluids are an essential integral part . How to simulate fluid dynamics accurately has long been studied by researchers and a large class of numerical models have been developed . However , computing high-quality fluid simulation is still computationally expen... | The authors propose a learned model specialized on learning Lagrangian fluid dynamics for incompressible fluids. The model is a hybrid between a simulator with explicit advection, collision and pressure correction stages, and a learned model, trained by supervising each of those stages. The authors demonstrate improved... | SP:92bb35142d496d7afaa07a298a3bffabd00ec352 |
Learning Lagrangian Fluid Dynamics with Graph Neural Networks | 1 INTRODUCTION . For many science and engineering problems , fluids are an essential integral part . How to simulate fluid dynamics accurately has long been studied by researchers and a large class of numerical models have been developed . However , computing high-quality fluid simulation is still computationally expen... | The paper deals with the prediction of 3D Lagrangian Fluid Simulations. Therefore the problem is divided into 3 subproblems, oriented on numerical simulations. An advection part, where the acceleration of the particles is calculated, a collision step, where the boundary effects are included, and a pressure prediction p... | SP:92bb35142d496d7afaa07a298a3bffabd00ec352 |
Multimodal Attention for Layout Synthesis in Diverse Domains | We address the problem of scene layout generation for diverse domains such as images , mobile applications , documents and 3D objects . Most complex scenes , natural or human-designed , can be expressed as a meaningful arrangement of simpler compositional graphical primitives . Generating a new layout or extending an e... | This work proposes a model to generate scene layouts by treating the scene as a composition of primitives, such as instance class, coordinates or scales. The model is a Transformer architecture, that attends on all previously predicted or given instance primitives. The probability of a scene layout is defined with a jo... | SP:821ad1017b8aa20f5b6bc3fcc56844ae87d983e2 |
Multimodal Attention for Layout Synthesis in Diverse Domains | We address the problem of scene layout generation for diverse domains such as images , mobile applications , documents and 3D objects . Most complex scenes , natural or human-designed , can be expressed as a meaningful arrangement of simpler compositional graphical primitives . Generating a new layout or extending an e... | This paper presents an auto-regressive method for generating layouts by sequentially synthesizing new elements. The architecture is not dramatically new, but it is well-justified and analyzed, and there are some interesting tweaks. The results are strongest in that they show good performance of essentially the same arc... | SP:821ad1017b8aa20f5b6bc3fcc56844ae87d983e2 |
SGD on Neural Networks learns Robust Features before Non-Robust | 1 INTRODUCTION . Neural networks have achieved state of the art performance on tasks spanning an array of domains like computer vision , translation , speech recognition , robotics , and playing board games ( Krizhevsky et al . ( 2012 ) ; Vaswani et al . ( 2017 ) ; Graves et al . ( 2013 ) ; Silver et al . ( 2016 ) ) . ... | This work studies the learning dynamics of neural networks in terms of robust and non-robust features. In particular, the authors argue that depending on various factors (e.g. learning rate, data augmentation), neural networks will have learning dynamics according to 1 of 2 pathways. Neural networks will either (1) fir... | SP:f26a952abe712256ad3046d86187c08c6eb2e395 |
SGD on Neural Networks learns Robust Features before Non-Robust | 1 INTRODUCTION . Neural networks have achieved state of the art performance on tasks spanning an array of domains like computer vision , translation , speech recognition , robotics , and playing board games ( Krizhevsky et al . ( 2012 ) ; Vaswani et al . ( 2017 ) ; Graves et al . ( 2013 ) ; Silver et al . ( 2016 ) ) . ... | The paper posits some phenomena on neural network training: 1. With some proper regularizing effect, NN training tends to learn predictive robust features (and weakly predictive non-robust features) first and non-robust features next. 2. Without regularization, NN training does a similar thing as case 1 first but does ... | SP:f26a952abe712256ad3046d86187c08c6eb2e395 |
Neural Bayes: A Generic Parameterization Method for Unsupervised Learning | 1 INTRODUCTION . We introduce a generic parameterization called Neural Bayes that facilitates unsupervised learning from unlabeled data by categorizing them . Specifically , our parameterization implicitly maps samples from an observed random variable x to a latent discrete space z where the distribution p ( x ) gets s... | The paper introduces a new function $L(x)$ so that, when optimised under certain objectives defined over continuous observation $x$ and discrete latent $z$, learns the correct clustering probability $p(z|x)$. The loss functions considered are the Jensen-Fisher divergence and muture information. The authors introduces m... | SP:5b4768c8d71e9b044c50d77fb68d545370ca8329 |
Neural Bayes: A Generic Parameterization Method for Unsupervised Learning | 1 INTRODUCTION . We introduce a generic parameterization called Neural Bayes that facilitates unsupervised learning from unlabeled data by categorizing them . Specifically , our parameterization implicitly maps samples from an observed random variable x to a latent discrete space z where the distribution p ( x ) gets s... | This work introduces a parameterization called Neural Bayes that facilitates learning representations from unlabeled data by categorizing them, where each data point x is mapped to a latent discrete variable z such that the distribution p(x) is segmented into a finite number of conditional distributions. Imposing diffe... | SP:5b4768c8d71e9b044c50d77fb68d545370ca8329 |
Don't stack layers in graph neural networks, wire them randomly | 1 INTRODUCTION . Data defined over the nodes of graphs are ubiquitous . Social network profiles ( Hamilton et al. , 2017 ) , molecular interactions ( Duvenaud et al. , 2015 ) , citation networks ( Sen et al. , 2008 ) , 3D point clouds ( Simonovsky & Komodakis , 2017 ) are just examples of a wide variety of data types w... | This paper utilizes Randomly Wired architectures to boost deep GNNs. Theoretical analyses verify that randomly wired architectures behave like path ensemble and it enables adaptive receptive field. Experimental results on three non-popular datasets demonstrate the strength of the proposed model. Overall, the idea is in... | SP:9d58dff3946cc3ebd5f5272deab9c5ccddd48efc |
Don't stack layers in graph neural networks, wire them randomly | 1 INTRODUCTION . Data defined over the nodes of graphs are ubiquitous . Social network profiles ( Hamilton et al. , 2017 ) , molecular interactions ( Duvenaud et al. , 2015 ) , citation networks ( Sen et al. , 2008 ) , 3D point clouds ( Simonovsky & Komodakis , 2017 ) are just examples of a wide variety of data types w... | The paper proposes a new method for building graph convolutional neural networks. It shows, that during the building of the network, instead of stacking many layers and adding the residual connection between them, one could employ a randomly-wired architecture, that can be a more effective way to increase the capacity ... | SP:9d58dff3946cc3ebd5f5272deab9c5ccddd48efc |
On Disentangled Representations Learned From Correlated Data | Despite impressive progress in the last decade , it still remains an open challenge to build models that generalize well across multiple tasks and datasets . One path to achieve this is to learn meaningful and compact representations , in which different semantic aspects of data are structurally disentangled . The focu... | The paper studies the behaviour of disentanglement methods and metrics on data where a couple of factors of variation (FoV) are correlated, a more realistic setup compared to the usual independent FoV setting in the literature. The paper shows how the correlation in the FoV is reflected in the representations learned b... | SP:ca83623b552cb6bd000d5a67fd81e41a6d7b1e7a |
On Disentangled Representations Learned From Correlated Data | Despite impressive progress in the last decade , it still remains an open challenge to build models that generalize well across multiple tasks and datasets . One path to achieve this is to learn meaningful and compact representations , in which different semantic aspects of data are structurally disentangled . The focu... | This paper systematically presents a large-scale empirical study on the disentangled representation learning when the underlying factors are possibly entangled. From the results of purely unsupervised settings, the authors have discovered the shortcomings of the existing metrics of disentanglement as well as the poor l... | SP:ca83623b552cb6bd000d5a67fd81e41a6d7b1e7a |
Acceleration in Hyperbolic and Spherical Spaces | We further research on the acceleration phenomenon on Riemannian manifolds by introducing the first global first-order method that achieves the same rates as accelerated gradient descent in the Euclidean space for the optimization of smooth and geodesically convex ( g-convex ) or strongly g-convex functions defined on ... | This paper provides a generalization of AGD to constant sectional curvature spaces (or subsets of them), and proves the same global rates of convergence that hold in the Euclidean space. Additionally, they provide reductions for the bounded sectional curvature case. Their basic strategy involves the use of geodesic map... | SP:3f9266d190e590b01625de888376769d59737d81 |
Acceleration in Hyperbolic and Spherical Spaces | We further research on the acceleration phenomenon on Riemannian manifolds by introducing the first global first-order method that achieves the same rates as accelerated gradient descent in the Euclidean space for the optimization of smooth and geodesically convex ( g-convex ) or strongly g-convex functions defined on ... | This paper considered the problem of minimizing (strongly and non-strongly) geodesically convex functions on hyperbolic and spherical manifolds, manifolds of constant curvature 1 and -1, respectively, and proposed accelerated algorithms for such problems. In particular, the author(s) showed the proposed algorithms enjo... | SP:3f9266d190e590b01625de888376769d59737d81 |
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs | 1 INTRODUCTION . Our ability to understand deep learning systems lags considerably behind our ability to obtain practical outcomes with them . A breadth of approaches have been developed in attempts to better understand deep learning systems and render them more comprehensible to humans ( Yosinski et al. , 2015 ; Bau e... | This paper examines the impact of forcing units in a CNN to be more or less “class-selective” – i.e. respond preferentially to one image class compared to another. The approach taken is to include a regularizer in the loss that directly penalizes or encourages class selectivity in individual units. They report that pe... | SP:759f85692cb4edfe6521d013dbbb55e20a458a4b |
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs | 1 INTRODUCTION . Our ability to understand deep learning systems lags considerably behind our ability to obtain practical outcomes with them . A breadth of approaches have been developed in attempts to better understand deep learning systems and render them more comprehensible to humans ( Yosinski et al. , 2015 ; Bau e... | This paper asks the interesting question of whether you need individual neuron (or even population level) class selectivity at intermediate stages in order to have good classification performance. The authors introduce a regularization term to the loss that controls the amount of selectivity in the units of the network... | SP:759f85692cb4edfe6521d013dbbb55e20a458a4b |
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers | 1 INTRODUCTION . Reinforcement learning ( RL ) can automate the acquisition of complex behavioral policies through real-world trial-and-error experimentation . However , many domains where we would like to learn policies are not amenable to such trial-and-error learning , because the errors are too costly : from autono... | This paper proposes a method for domain adaptation in RL where the source and target domains differ only in the transition distriubtions. A theoretical derivation based on RL as probabilistic inference is presented that starts with the objective of matching the desired distribution of trajectories in the target domain ... | SP:a39d669cce510debfadda370c1cb47d2eb960795 |
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers | 1 INTRODUCTION . Reinforcement learning ( RL ) can automate the acquisition of complex behavioral policies through real-world trial-and-error experimentation . However , many domains where we would like to learn policies are not amenable to such trial-and-error learning , because the errors are too costly : from autono... | The paper introduces DARC, a domain transfer algorithm motivated by maximum entropy RL. By introducing classifiers for the target and source domain, the reward function in the source domain can be modified such that it restricts the behavior of the optimized policy to transitions that reflect the target domain. In this... | SP:a39d669cce510debfadda370c1cb47d2eb960795 |
Non-Local Graph Neural Networks | 1 INTRODUCTION . Graph neural networks ( GNNs ) process graphs and map each node to an embedding vector ( Zhang et al. , 2018b ; Wu et al. , 2019 ) . These node embeddings can be directly used for node-level applications , such as node classification ( Kipf & Welling , 2017 ) and link prediction ( Schütt et al. , 2017... | This paper targets on addressing the node embedding problem in disassortative graphs. A non-local aggregation framework is proposed, since local aggregation may be harmful for some disassortative graphs. To address the high computational cost in the recent Geom-GCN model that has an attention-like step to compute the E... | SP:4341b2c3554d27983bb5077f0cb3448c0c764823 |
Non-Local Graph Neural Networks | 1 INTRODUCTION . Graph neural networks ( GNNs ) process graphs and map each node to an embedding vector ( Zhang et al. , 2018b ; Wu et al. , 2019 ) . These node embeddings can be directly used for node-level applications , such as node classification ( Kipf & Welling , 2017 ) and link prediction ( Schütt et al. , 2017... | The goal of the paper is to perform node classification for graphs. The authors propose a strategy to augment message passing graph neural networks with information from non-local nodes in the graph - with a focus on dis-assortative graphs. Dis-assortative graphs are graph datasets - where nodes with identical node lab... | SP:4341b2c3554d27983bb5077f0cb3448c0c764823 |
On the Landscape of Sparse Linear Networks | 1 INTRODUCTION . Deep neural networks ( DNNs ) have achieved remarkable empirical successes in the domains of computer vision , speech recognition , and natural language processing , sparking great interests in the theory behind their architectures and training . However , DNNs are often found to be highly overparamete... | This paper studies the loss landscapes of sparse linear networks. It proves that under squared loss, (1) spurious local minimum does not exist when the output dimension is one, or with separated first layer and orthogonal training data; and (2) for two-layer sparse linear networks, the good property in (1) does not exi... | SP:ef9027da9feec26a1fe583b9cd8c77e260bdc00f |
On the Landscape of Sparse Linear Networks | 1 INTRODUCTION . Deep neural networks ( DNNs ) have achieved remarkable empirical successes in the domains of computer vision , speech recognition , and natural language processing , sparking great interests in the theory behind their architectures and training . However , DNNs are often found to be highly overparamete... | This paper studies the optimization landscape of (deep) sparse linear networks. The study of sparse neural networks is well motivated: on the one hand, there is a lot of experimental evidence that the loss of the trained network does not decrease much after removing a large subset of the connections; on the other hand,... | SP:ef9027da9feec26a1fe583b9cd8c77e260bdc00f |
Generating Adversarial Computer Programs using Optimized Obfuscations | 1 INTRODUCTION . Machine learning ( ML ) models are increasingly being used for software engineering tasks . Applications such as refactoring programs , auto-completing them in editors , and synthesizing GUI code have benefited from ML models trained on large repositories of programs , sourced from popular websites lik... | This work tackles the problem of adversarial attacks against ML models for code-understanding tasks, such as function summarization. It formulates the problem as the adversarial application of existing semantics-preserving program transformations (e.g., renaming variables), by jointly optimizing on the location of such... | SP:598a0c59ed1b2fb08626115179948768d09f0e45 |
Generating Adversarial Computer Programs using Optimized Obfuscations | 1 INTRODUCTION . Machine learning ( ML ) models are increasingly being used for software engineering tasks . Applications such as refactoring programs , auto-completing them in editors , and synthesizing GUI code have benefited from ML models trained on large repositories of programs , sourced from popular websites lik... | * This paper proposes an optimization problem to adopt insert/replace operations (program obfuscations) to generate adversarial programs. They apply it to the task of program summarization, and show that they outperform the existing baseline published in 2020. In particular, one of the main contributions is the identif... | SP:598a0c59ed1b2fb08626115179948768d09f0e45 |
Domain-Free Adversarial Splitting for Domain Generalization | Domain generalization is an approach that utilizes several source domains to train the learner to be generalizable to unseen target domain to tackle domain shift issue . It has drawn much attention in machine learning community . This paper aims to learn to generalize well to unseen target domain without relying on the... | This paper focuses on domain generalization, targeting the challenging scenario where the training set might not include different sources; even under the presence of different sources, the problem formulation does not takes into account domain labels. The proposed solution is based on meta-learning, following the path... | SP:dc605d174368de20c31edca06ef90fc18fb79faa |
Domain-Free Adversarial Splitting for Domain Generalization | Domain generalization is an approach that utilizes several source domains to train the learner to be generalizable to unseen target domain to tackle domain shift issue . It has drawn much attention in machine learning community . This paper aims to learn to generalize well to unseen target domain without relying on the... | This paper proposes to unify adversarial training and meta-learning in domain-free generalization where labels of source domains are unavailable. To maximize the domain shift between the subsets of meta-train and meta-val, adversarial training is leveraged to find the worst-case train/val splits. Extensive experiments ... | SP:dc605d174368de20c31edca06ef90fc18fb79faa |
Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search | 1 INTRODUCTION . The computer vision community has witnessed substantial progress in object detection in recent years . The advances for the architecture design , e.g . two-stage detectors ( Ren et al. , 2015 ; Cai & Vasconcelos , 2018 ) and one-stage detectors ( Lin et al. , 2017b ; Tian et al. , 2019 ) , have remarka... | This paper proposes to use an evolutionary search algorithm to search for better loss functions for the classification and regression branch of an object detector. The algorithm starts with 20 primitive mathematical operations. Due to the highly sparse action space, the vanilla evolutionary algorithm would take a long ... | SP:dec287e2fe3b34942440388a7e79031e833dc718 |
Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search | 1 INTRODUCTION . The computer vision community has witnessed substantial progress in object detection in recent years . The advances for the architecture design , e.g . two-stage detectors ( Ren et al. , 2015 ; Cai & Vasconcelos , 2018 ) and one-stage detectors ( Lin et al. , 2017b ; Tian et al. , 2019 ) , have remarka... | This paper proposes to automatically discover proper loss functions for object detection. It first designs some unit mathematical operations as search space, and then performs evolutionary algorithm to discover well-performed loss functions for the object detection tasks. Different from image classification, one needs ... | SP:dec287e2fe3b34942440388a7e79031e833dc718 |
Meta-Active Learning in Probabilistically-Safe Optimization | 1 INTRODUCTION . Safe and efficient control of a novel systems with latent dynamics is an important objective in domains from healthcare to robotics . In healthcare , deep brain stimulation devices implanted in the brain can improve memory deficits in patients with Alzheimers ( Posporelis et al. , 2018 ) and responsive... | This paper presents a meta-active learning approach to obtain an LSTM-based embedding of a dynamic system and use a chance-constrained (probabilistic safe) optimization to find optimal control configuration via applying mixed-inter linear programming (MILP) on the learned embeddings of the dynamic system. The main idea... | SP:c1297729b15bbaece175e784cd5f7db7f395fede |
Meta-Active Learning in Probabilistically-Safe Optimization | 1 INTRODUCTION . Safe and efficient control of a novel systems with latent dynamics is an important objective in domains from healthcare to robotics . In healthcare , deep brain stimulation devices implanted in the brain can improve memory deficits in patients with Alzheimers ( Posporelis et al. , 2018 ) and responsive... | In this work, a meta active learning approach is proposed to learn the hidden dynamics of control systems, where safety is also a major concern. The main idea lies in doing meta-learning with Q-learning, meanwhile selecting safe actions by solving a mixed-integer linear programming problem. The performance of the propo... | SP:c1297729b15bbaece175e784cd5f7db7f395fede |
Categorical Normalizing Flows via Continuous Transformations | 1 INTRODUCTION . Normalizing Flows have been popular for tasks with continuous data like image modeling ( Dinh et al. , 2017 ; Kingma and Dhariwal , 2018 ; Ho et al. , 2019 ) and speech generation ( Kim et al. , 2019 ; Prenger et al. , 2019 ) by providing efficient parallel sampling and exact density evaluation . The c... | This work uses the idea of variational inference to map categorical data to continuous space affording the use of normalizing flows. Authors use several ideas to increase their framework's applicability--factorized distribution assumption, use of multi-scale architecture for step-generation, and permutation invariant c... | SP:a27b9b91520ec4d7e1cabce40411ff8a10dea9c8 |
Categorical Normalizing Flows via Continuous Transformations | 1 INTRODUCTION . Normalizing Flows have been popular for tasks with continuous data like image modeling ( Dinh et al. , 2017 ; Kingma and Dhariwal , 2018 ; Ho et al. , 2019 ) and speech generation ( Kim et al. , 2019 ; Prenger et al. , 2019 ) by providing efficient parallel sampling and exact density evaluation . The c... | The paper considers the problem of modeling discrete distributions with normalizing flows. Authors propose a novel framework “Categorical Normalizing Flows”, i.e CNF. By jointly modeling a mapping to continuous latent space, and the likelihood of flows CNF solves some of the bottlenecks in current algorithms. With expe... | SP:a27b9b91520ec4d7e1cabce40411ff8a10dea9c8 |
DyHCN: Dynamic Hypergraph Convolutional Networks | 1 INTRODUCTION . Graph Convolutional Network ( GCN ) Scarselli et al . ( 2008 ) extends deep neural networks to process graph data , which encodes the relations between nodes via propagating node features over the graph structure . GCN has become a promising solution in a wide spectral of graph analytic tasks , such as... | The paper extends over hypergraph convolutional networks (HCN) by adding a temporal evolution module in order to solve prediction tasks in a dynamic environment. The main part of the paper is the description of the proposed system. It is composed of a HCN for computing node embeddings at each time step and a LSTM as th... | SP:6c4659d71144bea924d9e77ee2be1bd6d11cf7f0 |
DyHCN: Dynamic Hypergraph Convolutional Networks | 1 INTRODUCTION . Graph Convolutional Network ( GCN ) Scarselli et al . ( 2008 ) extends deep neural networks to process graph data , which encodes the relations between nodes via propagating node features over the graph structure . GCN has become a promising solution in a wide spectral of graph analytic tasks , such as... | This paper proposes a method called DyHCN for learning dynamic hypergraph convolutional networks where the hypergraph structure is allowed to evolve over time. The interactions within each hyper edge, that between nodes, as well as related are used to learn the hypergaph embedding. The evolution of the centroid nodes i... | SP:6c4659d71144bea924d9e77ee2be1bd6d11cf7f0 |
Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests | 1 INTRODUCTION . Psychologists have employed mental rotation tests for decades ( Shepard & Metzler , 1971 ) as a powerful tool for devising how the human mind interprets and ( internally ) manipulates three dimensional representations of the world . Instead of using these test to probe the human capacity for mental 3D ... | The paper explores the problem of visual question answering from another perspective. Similar to VQA, a system is provided with a scene and a question. However, the difference is that the question needs to be answered from a viewpoint different from the one provided. Hence, the system needs to perform “mental rotation”... | SP:5542cb8de7d232cde44071f0612827309298e98b |
Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests | 1 INTRODUCTION . Psychologists have employed mental rotation tests for decades ( Shepard & Metzler , 1971 ) as a powerful tool for devising how the human mind interprets and ( internally ) manipulates three dimensional representations of the world . Instead of using these test to probe the human capacity for mental 3D ... | The paper studies visual question answering focusing on answering questions in a reference image of a different viewpoint. They propose a new dataset CLEVR-MRT drawing motivation from the well-known visual reasoning dataset CLEVR to illustrate the idea in which they have full control of the changes of viewpoints in an ... | SP:5542cb8de7d232cde44071f0612827309298e98b |
Understanding Over-parameterization in Generative Adversarial Networks | A broad class of unsupervised deep learning methods such as Generative Adversarial Networks ( GANs ) involve training of overparameterized models where the number of parameters of the model exceeds a certain threshold . Indeed , most successful GANs used in practice are trained using overparameterized generator and dis... | This paper studies how over-parameterization plays a role in GAN training. Theoretically, it shows that a GAN with over-parameterized 1-layer neural network generator and a linear discriminator can converge to global saddle point via stochastic optimisation. Similar results are obtained for nonlinear generators and dis... | SP:8a6de840ca758da3973655a8a478e83f8edde474 |
Understanding Over-parameterization in Generative Adversarial Networks | A broad class of unsupervised deep learning methods such as Generative Adversarial Networks ( GANs ) involve training of overparameterized models where the number of parameters of the model exceeds a certain threshold . Indeed , most successful GANs used in practice are trained using overparameterized generator and dis... | This paper studies the effect of model over-parametrization in GANs. While there is a lot of work on this in the supervised learning setting of classification/regression there is not much in the GAN framework where the minimax objective function complicates such an analysis. This paper considers two types of training o... | SP:8a6de840ca758da3973655a8a478e83f8edde474 |
Hierarchical Autoregressive Modeling for Neural Video Compression | 1 INTRODUCTION . Recent advances in deep generative modeling have seen a surge of applications , including learningbased compression . Generative models have already demonstrated empirical improvements in image compression , outperforming classical codecs ( Minnen et al. , 2018 ; Yang et al. , 2020d ) , such as BPG ( B... | In this paper, the authors provide a new interpretation of existing video compression models. Their perspective is that a video decoder is a stochastic temporal autoregressive model with latent variables. The introduced latent variables could be either used for providing more expressive power for 1) motion estimation&c... | SP:65e92cbe15e2f0237433a41149d1d68ded0cc51c |
Hierarchical Autoregressive Modeling for Neural Video Compression | 1 INTRODUCTION . Recent advances in deep generative modeling have seen a surge of applications , including learningbased compression . Generative models have already demonstrated empirical improvements in image compression , outperforming classical codecs ( Minnen et al. , 2018 ; Yang et al. , 2020d ) , such as BPG ( B... | In this paper, the authors focus on the problem of lossy video compression. To this end they propose the application of latent variable sequential generative models, specifically autoregressive flows to compress video streams. They evaluate variations of these models quantitatively including their own proposed version ... | SP:65e92cbe15e2f0237433a41149d1d68ded0cc51c |
Free Lunch for Few-shot Learning: Distribution Calibration | 1 INTRODUCTION Table 1 : The class mean similarity ( “ mean sim ” ) and class variance similarity ( “ var sim ” ) between Arctic fox and different classes . Arctic fox mean sim var sim white wolf 97 % 97 % malamute 85 % 78 % lion 81 % 70 % meerkat 78 % 70 % jellyfish 46 % 26 % orange 40 % 19 % beer bottle 34 % 11 % Lea... | The paper proposes a method to calibrate the underlying distribution of a few samples in the few-shot classification scenario. The idea is to estimate a feature distribution of a few samples of a novel class from base class distributions. The authors assume that every dimension in the feature vector follows a Gaussian ... | SP:98a52d7970d0d39f8e14f6b5679f8383a3f0e8b1 |
Free Lunch for Few-shot Learning: Distribution Calibration | 1 INTRODUCTION Table 1 : The class mean similarity ( “ mean sim ” ) and class variance similarity ( “ var sim ” ) between Arctic fox and different classes . Arctic fox mean sim var sim white wolf 97 % 97 % malamute 85 % 78 % lion 81 % 70 % meerkat 78 % 70 % jellyfish 46 % 26 % orange 40 % 19 % beer bottle 34 % 11 % Lea... | This paper identifies the problem of biased distributions in few-shot learning and proposes to fix it. In few-shot learning, only a few samples per class are available; this makes estimating the class distribution difficult. The paper proposes a distribution calibration algorithm that makes use of the meta-train class ... | SP:98a52d7970d0d39f8e14f6b5679f8383a3f0e8b1 |
TOWARDS NATURAL ROBUSTNESS AGAINST ADVERSARIAL EXAMPLES | 1 INTRODUCTION . Deep neural networks have made great progress in numerous domains of machine learning , especially in computer vision . But Szegedy et al . ( 2013 ) found that most of the existing state-of-theart neural networks are easily fooled by adversarial examples that generated by putting only very small pertur... | This paper offers an interesting viewpoint of adversarial robustness by comparing neural networks with skip connections such as ResNet with their Neural ODE counterparts. The authors analyze the different behaviors of the networks through their Lipschitz constants. They also try to support their claims that Neural ODEs... | SP:5e3798130b00275f58f296666d614d56147ec57a |
TOWARDS NATURAL ROBUSTNESS AGAINST ADVERSARIAL EXAMPLES | 1 INTRODUCTION . Deep neural networks have made great progress in numerous domains of machine learning , especially in computer vision . But Szegedy et al . ( 2013 ) found that most of the existing state-of-theart neural networks are easily fooled by adversarial examples that generated by putting only very small pertur... | This paper uses theoretical grounding, starting with Lipshitz continuity-based assumptions on residual connections, to show why such architectures are more susceptible to adversarial inputs. In the process, the authors draw a parallel between these residual connections and neural ODEs, showing how the latter can circum... | SP:5e3798130b00275f58f296666d614d56147ec57a |
Continual Memory: Can We Reason After Long-Term Memorization? | 1 INTRODUCTION . In recent years , the tremendous progress of neural networks has enabled machines to perform reasoning given a query Q and the input contents X , e.g. , infer the answer of given questions from the text/video stream in text/video question answering ( Seo et al. , 2016 ; Le et al. , 2020b ) , or predict... | To approach "reasoning after memorization", the paper presents a Continual Memory (CM) framework using a memory-augmented neural network (MANN) and self-supervised training. In particular, the CM compresses the input sequence into a matrix memory using self-attention mechanisms and gated recurrent update (GRU). Then ... | SP:75b5d32a5a6bc3373309ee3e9ad7507d23221f19 |
Continual Memory: Can We Reason After Long-Term Memorization? | 1 INTRODUCTION . In recent years , the tremendous progress of neural networks has enabled machines to perform reasoning given a query Q and the input contents X , e.g. , infer the answer of given questions from the text/video stream in text/video question answering ( Seo et al. , 2016 ; Le et al. , 2020b ) , or predict... | In this paper, the authors propose the Continual Memory (CM) targeted towards a reasoning scenario called “reasoning after memorization”. The main goal of CM is to enable long-term memorization as opposed to memory networks that suffer from gradual forgetting. They evaluate their model both on synthetic data as well as... | SP:75b5d32a5a6bc3373309ee3e9ad7507d23221f19 |
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bitwise Regularization | 1 INTRODUCTION . Deep neural networks have achieved great success in various computer vision applications such as image classification , object detection/segmentation , pose estimation , action recognition , and so on . However , state-of-the-art neural network architectures require too much computation and memory to b... | This paper deals with network quantization. It proposes Semi-Relaxed Quantization (SRQ) that uses a multi-class straight-through estimator to effectively reduce the bias and variance, along with a new regularization technique, DropBits that replaces dropout regularization to randomly drop the bits. Extensive experimen... | SP:722584f20a74efbfb6e50fb795aa33a39d73f13b |
Semi-Relaxed Quantization with DropBits: Training Low-Bit Neural Networks via Bitwise Regularization | 1 INTRODUCTION . Deep neural networks have achieved great success in various computer vision applications such as image classification , object detection/segmentation , pose estimation , action recognition , and so on . However , state-of-the-art neural network architectures require too much computation and memory to b... | This work presents 1) Semi-Relaxed Quantization (SRQ), a method that targets learning low-bit neural networks, 2) DropBits, a method that performs dropout-like regularization on the bit width of the quantizers with an option to also automatically optimise the bit-width per layer according to the data, and 3) quantised ... | SP:722584f20a74efbfb6e50fb795aa33a39d73f13b |
Adversarial Boot Camp: label free certified robustness in one epoch | 1 Introduction . Neural networks are very accurate on image classification tasks , but they are vulnerable to adversarial perturbations , i.e . small changes to the model input leading to misclassification ( Szegedy et al. , 2014 ) . Adversarial training ( Madry et al. , 2018 ) improves robustness , at the expense of a... | The paper claims that a (computationally intractable) randomized smoothing of any classifier can be distilled into the (deterministic) classifier itself via fine-tuning it with gradient penalty. This is motivated by a theoretical result that Gaussian smoothing of a classifier is equivalent to solving a certain heat equ... | SP:45cbc9c97027fe59ce2ce7f8a02d9257d3460a4c |
Adversarial Boot Camp: label free certified robustness in one epoch | 1 Introduction . Neural networks are very accurate on image classification tasks , but they are vulnerable to adversarial perturbations , i.e . small changes to the model input leading to misclassification ( Szegedy et al. , 2014 ) . Adversarial training ( Madry et al. , 2018 ) improves robustness , at the expense of a... | Randomized smoothing is the major way to certify the robustness of large scale networks, however, it requires sampling from Gaussian distribution many times, which is not fast enough for real-time inference. This paper uses a regularized loss to get deterministic Gaussian averaged results. This paper points out an inte... | SP:45cbc9c97027fe59ce2ce7f8a02d9257d3460a4c |
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows | 1 INTRODUCTION . Classical time series forecasting methods such as those in Hyndman & Athanasopoulos ( 2018 ) typically provide univariate forecasts and require hand-tuned features to model seasonality and other parameters . Time series models based on recurrent neural networks ( RNN ) , like LSTM ( Hochreiter & Schmid... | This work explores combining an RNN and a neural density estimator for forecasting in multivariate time series. RNN is stacked with a density estimator, MAF for best results, to forecast density of a multivariate time series at future time steps. In addition, variations of the architecture with attention and other dens... | SP:a685e4a6a1f6f3d69a9f0968145b6afd805dc5ab |
Multivariate Probabilistic Time Series Forecasting via Conditioned Normalizing Flows | 1 INTRODUCTION . Classical time series forecasting methods such as those in Hyndman & Athanasopoulos ( 2018 ) typically provide univariate forecasts and require hand-tuned features to model seasonality and other parameters . Time series models based on recurrent neural networks ( RNN ) , like LSTM ( Hochreiter & Schmid... | The paper proposes a method to provide probabilistic forecasts of multivariate time-series taking dependencies between series into account even for large dimensions. The approach consists in using a normalizing flow to model the distribution of observations at a time-step condition on a state that can be obtained eithe... | SP:a685e4a6a1f6f3d69a9f0968145b6afd805dc5ab |
Neural Lyapunov Model Predictive Control | 1 INTRODUCTION . Control systems comprise of safety requirements that need to be considered during the controller design process . In most applications , these are in the form of state/input constraints and convergence to an equilibrium point , a specific set or a trajectory . Typically , a control strategy that violat... | This paper addresses the question of how to stabilize a system in a vicinity of an equilibrium. While the majority of reinforcement learning algorithms rely on trial and error, which may damage the system, the authors introduce an algorithm for safe exploration and control. A traditional approach in model-based RL is t... | SP:1e88ff3d6daf6ca26d2f9d504c5ff282af5d3659 |
Neural Lyapunov Model Predictive Control | 1 INTRODUCTION . Control systems comprise of safety requirements that need to be considered during the controller design process . In most applications , these are in the form of state/input constraints and convergence to an equilibrium point , a specific set or a trajectory . Typically , a control strategy that violat... | In this paper the author proposed an MPC algorithm in which both the dynamics function and the Lyapunov function are parameterized with neural networks.. Specifically leveraging the results of Lyapunov networks (2018 CORL paper: https://arxiv.org/abs/1808.00924) for learning Lyapunov functions, the authors derived an M... | SP:1e88ff3d6daf6ca26d2f9d504c5ff282af5d3659 |
Understanding and Mitigating Accuracy Disparity in Regression | 1 INTRODUCTION . Recent progress in machine learning has led to its widespread use in many high-stakes domains , such as criminal justice , healthcare , student loan approval , and hiring . Meanwhile , it has also been widely observed that accuracy disparity could occur inadvertently under various scenarios in practice... | This paper deals with a fair regression problem in which the accuracy disparity is employed as a fairness measure. The authors derived the upper and lower bounds on the difference of accuracy between groups to demonstrate that imbalance in the groups' sizes leads to accuracy disparity. Furthermore, they propose learnin... | SP:e27fedc58e99952aaa61b87bb613b7e2c3e23126 |
Understanding and Mitigating Accuracy Disparity in Regression | 1 INTRODUCTION . Recent progress in machine learning has led to its widespread use in many high-stakes domains , such as criminal justice , healthcare , student loan approval , and hiring . Meanwhile , it has also been widely observed that accuracy disparity could occur inadvertently under various scenarios in practice... | This paper theoretically and empirically studies accuracy disparity in regression problems. It proves an information-theoretic lower bound on the joint error and a complementary upper bound on the error gap across groups to depict the feasible region of group-wise errors. It further proposes to achieve accuracy parity ... | SP:e27fedc58e99952aaa61b87bb613b7e2c3e23126 |
CaPC Learning: Confidential and Private Collaborative Learning | Machine learning benefits from large training datasets , which may not always be possible to collect by any single entity , especially when using privacy-sensitive data . In many contexts , such as healthcare and finance , separate parties may wish to collaborate and learn from each other ’ s data but are prevented fro... | This paper works on the problem of collaborative learning while preserving both confidentiality and privacy of the data points. It combines techniques from secure multi-party computation and differential privacy for the same, and improves on confidential inference and PATE in the process. The new technique is called Ca... | SP:e1b814eef558840aef2fba9092482c1b09b1ef30 |
CaPC Learning: Confidential and Private Collaborative Learning | Machine learning benefits from large training datasets , which may not always be possible to collect by any single entity , especially when using privacy-sensitive data . In many contexts , such as healthcare and finance , separate parties may wish to collaborate and learn from each other ’ s data but are prevented fro... | The authors combine several cryptographic techniques to create a federated systems that allows several entities to run classification against all the model held be the participants without revealing information in the process. In particular, the sample to be classified is not revealed to any other party, and differenti... | SP:e1b814eef558840aef2fba9092482c1b09b1ef30 |
Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients | 1 INTRODUCTION . A data set sampled from a certain population is called biased if the subgroups of the population are sampled at proportions that are significantly different from their underlying population proportions . Applying machine learning algorithms naively to biased training data can raise serious concerns and... | This paper delves into a stability analysis of reweighting and resampling for overcoming imbalanced data in supervised learning. Reweighting employs the use of importance ratios to modify a samples weight to the training in turn changing the effective distribution. There are several resampling procedures which all have... | SP:aabb111652a2063c12c8faf92abc12e446d5d377 |
Why resampling outperforms reweighting for correcting sampling bias with stochastic gradients | 1 INTRODUCTION . A data set sampled from a certain population is called biased if the subgroups of the population are sampled at proportions that are significantly different from their underlying population proportions . Applying machine learning algorithms naively to biased training data can raise serious concerns and... | This paper provides an analysis of why resampling can be better than reweighting in some cases. By observing the behaviour of resampling and reweighting in simple optimizations with SGD, the theoretical results show that resampling tends to be more stable. The general analysis is based on SDE approximation. Experiments... | SP:aabb111652a2063c12c8faf92abc12e446d5d377 |
Data augmentation as stochastic optimization | 1 INTRODUCTION . Implementing gradient-based optimization in practice requires many choices . These include setting hyperparameters such as learning rate and batch size as well as specifying a data augmentation scheme , a popular set of techniques in which data is augmented ( i.e . modified ) at every step of optimizat... | .** Authors present a novel theoretical framework for assessing the effect of data augmentation (e.g. mini batch SGD), noise addition and the learning rate setup in gradient-based optimization with overparametrized models. Despite the analysis is only performed for linear regression, results extend the well-known Monro... | SP:a92ce63df0b4384bf0304661c8a8c80553377d57 |
Data augmentation as stochastic optimization | 1 INTRODUCTION . Implementing gradient-based optimization in practice requires many choices . These include setting hyperparameters such as learning rate and batch size as well as specifying a data augmentation scheme , a popular set of techniques in which data is augmented ( i.e . modified ) at every step of optimizat... | The paper considers stochastic gradient descent with noisy gradients. In contrast to the standard setting (e.g., gradient Langevin dynamics) where additive Gaussian noise is added to the model gradient, this work focuses on additive perturbations of data instances. As a result of this, the optimization objective change... | SP:a92ce63df0b4384bf0304661c8a8c80553377d57 |
Domain-slot Relationship Modeling using a Pre-trained Language Encoder for Multi-Domain Dialogue State Tracking | 1 INTRODUCTION . A task-oriented dialogue system is designed to help humans solve tasks by understanding their needs and providing relevant information accordingly . For example , such a system may assist its user with making a reservation at an appropriate restaurant by understanding the user ’ s needs for having a ni... | This paper proposed a new approach for modeling multi-domain dialogue state tracking by incorporating domain-slot relationship using a pre-trained language encoder. The proposed approach are based on using special tokens to mode l such relationship. Two kinds of special tokens are proposed to represent domain-slot pair... | SP:140100004dc307efd67790ef58f67929d3403c67 |
Domain-slot Relationship Modeling using a Pre-trained Language Encoder for Multi-Domain Dialogue State Tracking | 1 INTRODUCTION . A task-oriented dialogue system is designed to help humans solve tasks by understanding their needs and providing relevant information accordingly . For example , such a system may assist its user with making a reservation at an appropriate restaurant by understanding the user ’ s needs for having a ni... | In this paper, the authors proposed a multidomain state-tracking model that leverages the relationship among different domain-slot pairs. This is done by leveraging the full-attention step over the [CLS] special token and by providing all the domain-slot pairs as a special token to a pre-trained language model (Figure ... | SP:140100004dc307efd67790ef58f67929d3403c67 |
Learnable Embedding sizes for Recommender Systems | 1 INTRODUCTION . The success of deep learning-based recommendation models ( Zhang et al. , 2019 ) demonstrates their advantage in learning feature representations , especially for the most widely-used categorical features . These models utilize the embedding technique to map these sparse categorical features into real-... | The paper proposes PEP (Plug-in Embedding Pruning) to reduce the size of embedding table while incurring insignificant drop in accuracy. The related work is well summarized into Embedding Parameter Sharing and Embedding Size Selection methods and the motivation for the current approach is well explained. The paper draw... | SP:2e548e320d5da211ffed027de7f0c6b78935f205 |
Learnable Embedding sizes for Recommender Systems | 1 INTRODUCTION . The success of deep learning-based recommendation models ( Zhang et al. , 2019 ) demonstrates their advantage in learning feature representations , especially for the most widely-used categorical features . These models utilize the embedding technique to map these sparse categorical features into real-... | This paper proposed a novel approach to reduce size of the embedding table while not to drop in accuracy and computational optimization. Fixed-size embedding table has two problems, high memory usage cost and overfitting problem for those features that do not require too large representation. This paper recast the prob... | SP:2e548e320d5da211ffed027de7f0c6b78935f205 |
Adding Recurrence to Pretrained Transformers | 1 INTRODUCTION . Recent progress in NLP has been dominated by large pretrained transformer neural networks ( Vaswani et al. , 2017 ) , such as BERT ( Devlin et al. , 2019 ) , and GPT-2 ( Radford et al. , 2019 ) . However , these models have a memory footprint that is quadratic in input sequence length . Although archit... | The goal of this work is to enable existing pre-trained transformers (e.g. GPT-2) to operate over long input contexts. This is achieved by breaking the input sequence into segments and processing each segment through the transformers while allowing tokens in the current segment to attend over a summary vector of the to... | SP:8f5230bf3c19417980b10112488d1c7a8f1177f4 |
Adding Recurrence to Pretrained Transformers | 1 INTRODUCTION . Recent progress in NLP has been dominated by large pretrained transformer neural networks ( Vaswani et al. , 2017 ) , such as BERT ( Devlin et al. , 2019 ) , and GPT-2 ( Radford et al. , 2019 ) . However , these models have a memory footprint that is quadratic in input sequence length . Although archit... | The paper proposed to add a recurrent component to pretrained transformers. The component pools the hidden states of a context window and passes it to the next context window as an additional input to the self-attention layer. The component reduces the memory usage at both training and inference time, and enables the T... | SP:8f5230bf3c19417980b10112488d1c7a8f1177f4 |
On the Universality of Rotation Equivariant Point Cloud Networks | 1 INTRODUCTION . Designing neural networks that respect data symmetry is a powerful approach for obtaining efficient deep models . Prominent examples being convolutional networks which respect the translational invariance of images , graph neural networks which respect the permutation invariance of graphs ( Gilmer et a... | The authors introduce a framework for sufficient conditions for proving universality of a general class of neural networks that operate on point clouds which takes as input a set of coordinates of points and as output a feature for each point, such that the network is invariant to joint translation of the coordinates, ... | SP:eaac43a5cb483c71834b394b015d191cb8cbd815 |
On the Universality of Rotation Equivariant Point Cloud Networks | 1 INTRODUCTION . Designing neural networks that respect data symmetry is a powerful approach for obtaining efficient deep models . Prominent examples being convolutional networks which respect the translational invariance of images , graph neural networks which respect the permutation invariance of graphs ( Gilmer et a... | This paper mainly explores the representation ability of invariability of a point cloud network from the theoretical perspective. The universal approximation property for equivariant architectures under shape-preserving transformations is discussed. First, the authors derived two sufficient conditions for equivariant a... | SP:eaac43a5cb483c71834b394b015d191cb8cbd815 |
Implicit Gradient Regularization | 1 INTRODUCTION . The loss surface of a deep neural network is a mountainous terrain - highly non-convex with a multitude of peaks , plateaus and valleys ( Li et al. , 2018 ; Liu et al. , 2020 ) . Gradient descent provides a path through this landscape , taking discrete steps in the direction of steepest descent toward ... | This paper provides a unique perspective on the implicit regularization effect of gradient descent that has been observed and studied previously. The authors point out that the discrete steps taken by the gradient descent updates means that the path followed through the optimization landscape is not that of steepest de... | SP:49ef158a8170a8002d1111080db8009d5a6419d1 |
Implicit Gradient Regularization | 1 INTRODUCTION . The loss surface of a deep neural network is a mountainous terrain - highly non-convex with a multitude of peaks , plateaus and valleys ( Li et al. , 2018 ; Liu et al. , 2020 ) . Gradient descent provides a path through this landscape , taking discrete steps in the direction of steepest descent toward ... | The authors show the discrete steps of gradient descent implicitly regularize models by penalizing trajectories that have large loss-gradients, which is called Implicit Gradient Regularization in the paper. The authors adopt a standard argument from the backward error analysis of Runge-Kutta methods to show this phenom... | SP:49ef158a8170a8002d1111080db8009d5a6419d1 |
Laplacian Eigenspaces, Horocycles and Neuron Models on Hyperbolic Spaces | 1 INTRODUCTION . Conventional deep network techniques attempt to use architecture based on compositions of simple functions to learn representations of Euclidean data ( LeCun et al. , 2015 ) . They have achieved remarkable successes in a wide range of applications ( Hinton et al. , 2012 ; He et al. , 2016 ) . Geometric... | This paper develops a MLR based on hyperbolic geometry. The idea is based on well-known concept of horocycle and horospheres which are known to be hyperbolic counterpart of line and plane in Euclidean geometry (see Coxter). Then the authors show the universal approximation which kind of follows similarly from the Eucli... | SP:50fe6a0cf9b00e462adff4c4273b2604546b4023 |
Laplacian Eigenspaces, Horocycles and Neuron Models on Hyperbolic Spaces | 1 INTRODUCTION . Conventional deep network techniques attempt to use architecture based on compositions of simple functions to learn representations of Euclidean data ( LeCun et al. , 2015 ) . They have achieved remarkable successes in a wide range of applications ( Hinton et al. , 2012 ; He et al. , 2016 ) . Geometric... | This paper proposes new neural models for hyperbolic space, which unlike previous hyperbolic NN works, relies on the notion of horocycle in the Poincare disk. This novel framework has connections to spectral learnig in hyperbolic space. Representation theorems alla Cybenko for layers constructed from these neurons are ... | SP:50fe6a0cf9b00e462adff4c4273b2604546b4023 |
Variance Based Sample Weighting for Supervised Deep Learning | In the context of supervised learning of a function by a Neural Network ( NN ) , we claim and empirically justify that a NN yields better results when the distribution of the data set focuses on regions where the function to learn is steeper . We first traduce this assumption in a mathematically workable way using Tayl... | A method for computing sample learning weights based on variance is proposed. The method is model independent and a simple k-NN based estimator for the weights is derived. The authors justify their work by appealing to a novel generalisation bound. Overall the idea is interesting but the exposition needs to be signific... | SP:ea4d4d3798119498a6df81a19dcab2ae4978996c |
Variance Based Sample Weighting for Supervised Deep Learning | In the context of supervised learning of a function by a Neural Network ( NN ) , we claim and empirically justify that a NN yields better results when the distribution of the data set focuses on regions where the function to learn is steeper . We first traduce this assumption in a mathematically workable way using Tayl... | The authors introduce an algorithm called VBSW to re-weight a training data set in order to improve generalization. In summary, VBSW sets the weight of each example to be the sample variance of the labels of its k nearest neighbors. The nearest neighbors are chosen in the embedding space from the second-to-last layer o... | SP:ea4d4d3798119498a6df81a19dcab2ae4978996c |
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) has made remarkable advances in many domains , including arcade video games ( Mnih et al. , 2015 ) , complex continuous robot control ( Lillicrap et al. , 2016 ) and the game of Go ( Silver et al. , 2017 ) . Recently , many researchers put their efforts to extend the RL me... | The authors propose RMIX to deal with the randomness of rewards and the uncertainty in environments. RMIX learns the individual value distributions of each agent and uses a predictor to calculate the dynamic risk level. Given the individual value distribution and the risk level, a CVaR operator outputs the C value for ... | SP:2dc6337218afc973db75d973d08c0cdd7e55698b |
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) has made remarkable advances in many domains , including arcade video games ( Mnih et al. , 2015 ) , complex continuous robot control ( Lillicrap et al. , 2016 ) and the game of Go ( Silver et al. , 2017 ) . Recently , many researchers put their efforts to extend the RL me... | This paper proposes a new value-based method using risk measures in cooperative multi-agent reinforcement learning. The authors propose a new network structure that calculates global CVaR through individual distribution and learns risk-sensitized multi-agent policies. The authors also propose a new dynamic risk level p... | SP:2dc6337218afc973db75d973d08c0cdd7e55698b |
Concept Learners for Few-Shot Learning | 1 INTRODUCTION . Deep learning has reached human-level performance on domains with the abundance of large-scale labeled training data . However , learning on tasks with a small number of annotated examples is still an open challenge . Due to the lack of training data , models often overfit or are too simplistic to prov... | The paper presents a knowledge-driven prototypical learning strategy for few-shot classification tasks. The main idea of this work is to introduce a set of concepts defined in the subspaces of inputs and represent each class as a group of concept prototypes for few-shot learning. Following the prototypical networks, th... | SP:21f870f084d0b9b91f258cf893c66fd207570236 |
Concept Learners for Few-Shot Learning | 1 INTRODUCTION . Deep learning has reached human-level performance on domains with the abundance of large-scale labeled training data . However , learning on tasks with a small number of annotated examples is still an open challenge . Due to the lack of training data , models often overfit or are too simplistic to prov... | This paper introduces potential use of intermediate structured representation of input space called “concepts” which are most likely human-interpretable. This intermediate space is then used for few-shot learning instead of using only the input space. This leads to better classification performance on the task, and it ... | SP:21f870f084d0b9b91f258cf893c66fd207570236 |
Universal Sentence Representations Learning with Conditional Masked Language Model | 1 INTRODUCTION . Sentence embeddings map sentences into a vector space . The vectors capture rich semantic information that can be used to measure semantic textual similarity ( STS ) between sentences or train classifiers for a broad range of downstream tasks ( Conneau et al. , 2017 ; Subramanian et al. , 2018 ; Logesw... | This paper presents Conditional Masked Language Modeling (CMLM), which integrates sentence representation learning into MLM training by conditioning on the encoded vectors of adjacent sentences. It is shown that the English CMLM model achieves strong performance on SentEval, and outperforms models learned using (semi-)... | SP:9395fc883c2947587ff26fd36ce0fc797d062f3e |
Universal Sentence Representations Learning with Conditional Masked Language Model | 1 INTRODUCTION . Sentence embeddings map sentences into a vector space . The vectors capture rich semantic information that can be used to measure semantic textual similarity ( STS ) between sentences or train classifiers for a broad range of downstream tasks ( Conneau et al. , 2017 ; Subramanian et al. , 2018 ; Logesw... | The authors present conditional masked language modeling (CMLM), a new method for unsupervised pretraining, in which the skip-thought notion of conditioning on neighboring sentences is adopted for masked language modeling. The upshot of the proposed approach is that it generates single sentence embeddings that perform ... | SP:9395fc883c2947587ff26fd36ce0fc797d062f3e |
Measuring and Harnessing Transference in Multi-Task Learning | 1 INTRODUCTION . Deciding if two or more objectives should be trained together in a multi-task model , as well as choosing how that model ’ s parameters should be shared , is an inherently complex issue often left to human experts ( Zhang & Yang , 2017 ) . However , a human ’ s understanding of similarity is motivated ... | This paper studies the transferability in multi-task learning. They propose a metric, transference, to evaluate how tasks affect each other during multi-task training, and a method called IT-MTL which utilizes this metric to compute and improve lookahead loss changes. Although the proposed metric and method are interes... | SP:14a10829b5d4b5fcdf1c02720b767e6af2733a48 |
Measuring and Harnessing Transference in Multi-Task Learning | 1 INTRODUCTION . Deciding if two or more objectives should be trained together in a multi-task model , as well as choosing how that model ’ s parameters should be shared , is an inherently complex issue often left to human experts ( Zhang & Yang , 2017 ) . However , a human ’ s understanding of similarity is motivated ... | [Summary] This paper studies the problem of task relationship/transference in multi-task learning, by introducing a quantifiable measurement based on relative loss updates. A (nonsymmetric) task transference between task $i$ and task $j$ then can be computed by measuring the relative change of training loss of task $j... | SP:14a10829b5d4b5fcdf1c02720b767e6af2733a48 |
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness | 1 INTRODUCTION . Robust generalization can serve as an extension of tradition generalization , i.e. , Empirical Risk Minimization in the case of i.i.d . data ( Vapnik & Chervonenkis , 2015 ) , where the test environments might differ slightly or dramatically from the training environment ( Krueger et al. , 2020 ) . Imp... | This paper first studies the tradeoffs between two forms of spatial robustness, including robustness against Flow-based spatial attack and Rotation-Translation (RT) attack. In particular, it proposes an approach to account for both local and global spatial transformations in an integrated framework. In addition, the pa... | SP:e70a869dc8d81a0338d382ea6a761145ed8e59bd |
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness | 1 INTRODUCTION . Robust generalization can serve as an extension of tradition generalization , i.e. , Empirical Risk Minimization in the case of i.i.d . data ( Vapnik & Chervonenkis , 2015 ) , where the test environments might differ slightly or dramatically from the training environment ( Krueger et al. , 2020 ) . Imp... | This paper first provides explanations to the inherent tradeoff between rotation adversarial attack and sensitivity attacks/spatial transform attacks, through their differences in saliency maps. Further, the authors proposed to utilize pareto training to find the best tradeoff among the four dimensions: natural accurac... | SP:e70a869dc8d81a0338d382ea6a761145ed8e59bd |
Generative Language-Grounded Policy in Vision-and-Language Navigation with Bayes' Rule | 1 INTRODUCTION . Vision-and-language navigation ( Anderson et al. , 2018b ) is a task in which a computational model follows an instruction and performs a sequence of actions to reach the final objective . An agent is embodied in a realistic 3D environment , such as that from the Matterport 3D Simulator ( Chang et al. ... | The paper addresses the problem of vision-and-language navigation (Anderson et al., 2018). The idea of the paper is to use a generative policy where a distribution over all instruction tokens given the previous actions is computed. The agent takes the action that maximizes the probability of the current instruction. Th... | SP:737ec0b9d0df72ef8c1db34a89773a627105b240 |
Generative Language-Grounded Policy in Vision-and-Language Navigation with Bayes' Rule | 1 INTRODUCTION . Vision-and-language navigation ( Anderson et al. , 2018b ) is a task in which a computational model follows an instruction and performs a sequence of actions to reach the final objective . An agent is embodied in a realistic 3D environment , such as that from the Matterport 3D Simulator ( Chang et al. ... | The paper focuses on learning a navigation policy for a vision-and-language navigation problem. In this problem, the agent are given a language instruction and are asked to follow the instruction to navigation in a simulated 3D room. Unlike baselines which maximize the probability of selecting an action given an instru... | SP:737ec0b9d0df72ef8c1db34a89773a627105b240 |
Reset-Free Lifelong Learning with Skill-Space Planning | 1 INTRODUCTION . Intelligent agents , such as humans , continuously interact with the real world and make decisions to maximize their utility over the course of their lifetime . This is broadly the goal of lifelong reinforcement learning ( RL ) , which seeks to automatically learn artificial agents that can mimic the c... | The authors propose LiSP, a model-based planning method that performs model-predictive control using learned skills rather than actions. The skills are learned using DADS, with a modified reward function that additionally encourages all skills to stay within the support of training data to avoid sink states. The experi... | SP:6cad092c66273cdb0065834ee4459f1b76f8929d |
Reset-Free Lifelong Learning with Skill-Space Planning | 1 INTRODUCTION . Intelligent agents , such as humans , continuously interact with the real world and make decisions to maximize their utility over the course of their lifetime . This is broadly the goal of lifelong reinforcement learning ( RL ) , which seeks to automatically learn artificial agents that can mimic the c... | This paper presents a lifelong reinforcement learning framework in a non-stationary environment with non-episodic interactions. The proposed approach is to 1) learn "skills" - a world model - to maximize the intrinsic rewards using both online and offline data, and to 2) make best plans based on the learned world model... | SP:6cad092c66273cdb0065834ee4459f1b76f8929d |
On Learning Universal Representations Across Languages | 1 INTRODUCTION . Pre-trained models ( PTMs ) like ELMo ( Peters et al. , 2018 ) , GPT ( Radford et al. , 2018 ) and BERT ( Devlin et al. , 2019 ) have shown remarkable success of effectively transferring knowledge learned from large-scale unlabeled data to downstream NLP tasks , such as text classification ( Socher et ... | The work applies and adjusts contrastive learning in the subject area of pre-training language models. The work first identifies the challenges with the current landscape of Masked Language Models with limits to learning sentence-level representations and semantic alignments in sentences of different languages. To take... | SP:a17218a21d8f69f2848a248c8658df81c8a68924 |
On Learning Universal Representations Across Languages | 1 INTRODUCTION . Pre-trained models ( PTMs ) like ELMo ( Peters et al. , 2018 ) , GPT ( Radford et al. , 2018 ) and BERT ( Devlin et al. , 2019 ) have shown remarkable success of effectively transferring knowledge learned from large-scale unlabeled data to downstream NLP tasks , such as text classification ( Socher et ... | The paper proposes a pre-trained language model variant which extends XLM-R (multilingual masked model) with two new objectives. The main difference to most other models is that the new losses are contrastive losses (however, as pointed out by the authors, other contrastive losses had been used before in e.g. ELECTRA).... | SP:a17218a21d8f69f2848a248c8658df81c8a68924 |
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration | Policy entropy regularization is commonly used for better exploration in deep reinforcement learning ( RL ) . However , policy entropy regularization is sampleinefficient in off-policy learning since it does not take the distribution of previous samples stored in the replay buffer into account . In order to take advant... | The paper proposes DAC, an actor-critic method exploiting the replay buffer to do policy entropy regularisation. The main idea of DAC is to use the data from the replay buffer to induce a distribution $q(\cdot, s_t)$ and replace the entropy part of the Soft Actor-Critic objective with a convex combination of $q$ and $... | SP:cc6c0eb769a3da3f0e311fe6a4b96286f1f98d01 |
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration | Policy entropy regularization is commonly used for better exploration in deep reinforcement learning ( RL ) . However , policy entropy regularization is sampleinefficient in off-policy learning since it does not take the distribution of previous samples stored in the replay buffer into account . In order to take advant... | This paper considers the exploration efficiency issues in off-policy deep reinforcement learning (DRL). The authors identify a sample efficiency limitation in the classical entropy regularization, which does not take into account the existing samples in the replay buffer. To avoid repeated sampling of previously seen s... | SP:cc6c0eb769a3da3f0e311fe6a4b96286f1f98d01 |
A Strong On-Policy Competitor To PPO | 1 INTRODUCTION . With the development of deep reinforcement learning , lots of impressive results have been produced in a wide range of fields such as playing Atari game ( Mnih et al. , 2015 ; Hessel et al. , 2018 ) , controlling robotics ( Lillicrap et al. , 2015 ) , Go ( Silver et al. , 2017 ) , neural architecture s... | The authors replace the divergence-based constraint in trust region policy optimization model with an alternate distance measure, which is added to the objective function with a multiplier (beta). In fact, the parameter beta plays a role that is similar to a Lagrange multiplier, if the new distance measure is introduc... | SP:7286d578f6acf486a688b7631e16c483efb6a540 |
A Strong On-Policy Competitor To PPO | 1 INTRODUCTION . With the development of deep reinforcement learning , lots of impressive results have been produced in a wide range of fields such as playing Atari game ( Mnih et al. , 2015 ; Hessel et al. , 2018 ) , controlling robotics ( Lillicrap et al. , 2015 ) , Go ( Silver et al. , 2017 ) , neural architecture s... | This paper introduces POP3D, an on-policy policy gradient algorithm that is a variant of TRPO and PPO. While TRPO uses a particular penalty function to keep the policy from being updated too aggressively, POP3D uses an alternative objective function that lower bounds the square of the total variance divergence between ... | SP:7286d578f6acf486a688b7631e16c483efb6a540 |
Connection- and Node-Sparse Deep Learning: Statistical Guarantees | Neural networks are becoming increasingly popular in applications , but a comprehensive mathematical understanding of their potentials and limitations is still missing . In this paper , we study the prediction accuracies of neural networks from a statistical point of view . In particular , we establish statistical guar... | This paper studies the problem of estimating a vector valued regression function by neural networks. They provide a bound on the in-sample prediction error for a neural network estimator under two types of regularizations; one that induces connection sparsity and another that induces node sparsity. The in-sample error ... | SP:f6b70565d5b35e145ed9f8cae717dee238f0086c |
Connection- and Node-Sparse Deep Learning: Statistical Guarantees | Neural networks are becoming increasingly popular in applications , but a comprehensive mathematical understanding of their potentials and limitations is still missing . In this paper , we study the prediction accuracies of neural networks from a statistical point of view . In particular , we establish statistical guar... | This paper studies mean-squared-error bounds for neural networks with small $\ell_1$-norm. The use of $\ell_1$-norm constraint is analogous to the use of LASSO in sparse linear regression. They give a "mean-squared-error" bound because they only analyze a fixed-design setting (where the goal is only to analyze the effe... | SP:f6b70565d5b35e145ed9f8cae717dee238f0086c |
Neural Topic Model via Optimal Transport | 1 INTRODUCTION . As an unsupervised approach , topic modelling has enjoyed great success in automatic text analysis . In general , a topic model aims to discover a set of latent topics from a collection of documents , each of which describes an interpretable semantic concept . Topic models like Latent Dirichlet Allocat... | The paper proposes a neural topic model which log-likelihood is regularized by Sinkhorn distance, instead of following Variational AutoEncoder (VAE) approach. The proposed model is hence cannot be interpreted as a probabilistic generative model. Still, with respect to metrics such as Topic Coherence and Topic Diversity... | SP:a020f6bca5d85f83d595e5b724e32394009dcd7e |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.