paper_id string | title string | paper_url string | authors list | type string | primary_area string | abstract large_string | keywords list | TL;DR large_string | submission_number int64 | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|
qZzy5urZw9 | Robust Overfitting may be mitigated by properly learned smoothening | https://openreview.net/forum?id=qZzy5urZw9 | [
"Tianlong Chen",
"Zhenyu Zhang",
"Sijia Liu",
"Shiyu Chang",
"Zhangyang Wang"
] | Poster | null | A recent study (Rice et al., 2020) revealed overfitting to be a dominant phenomenon in adversarially robust training of deep networks, and that appropriate early-stopping of adversarial training (AT) could match the performance gains of most recent algorithmic improvements. This intriguing problem of robust overfittin... | [
"Robust Overfitting",
"Adversarial Training",
"Adversarial Robustness"
] | null | 2,638 | null | null |
lVgB2FUbzuQ | Predicting Infectiousness for Proactive Contact Tracing | https://openreview.net/forum?id=lVgB2FUbzuQ | [
"Yoshua Bengio",
"Prateek Gupta",
"Tegan Maharaj",
"Nasim Rahaman",
"Martin Weiss",
"Tristan Deleu",
"Eilif Benjamin Muller",
"Meng Qu",
"victor schmidt",
"Pierre-Luc St-Charles",
"hannah alsdurf",
"Olexa Bilaniuk",
"david buckeridge",
"gaetan caron",
"pierre luc carrier",
"Joumana Gho... | Spotlight | null | The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdowns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the v... | [
"covid-19",
"contact tracing",
"distributed inference",
"set transformer",
"deepset",
"epidemiology",
"applications",
"domain randomization",
"retraining",
"simulation"
] | null | 2,637 | 2010.12536 | title_snapshot |
tH6_VWZjoq | Local Search Algorithms for Rank-Constrained Convex Optimization | https://openreview.net/forum?id=tH6_VWZjoq | [
"Kyriakos Axiotis",
"Maxim Sviridenko"
] | Poster | null | We propose greedy and local search algorithms for rank-constrained convex optimization, namely solving $\underset{\mathrm{rank}(A)\leq r^*}{\min}\, R(A)$ given a convex function $R:\mathbb{R}^{m\times n}\rightarrow \mathbb{R}$ and a parameter $r^*$. These algorithms consist of repeating two steps: (a) adding a new rank... | [
"low rank",
"rank-constrained convex optimization",
"matrix completion"
] | null | 2,635 | 2101.06262 | title_snapshot |
vcopnwZ7bC | Learning Task Decomposition with Ordered Memory Policy Network | https://openreview.net/forum?id=vcopnwZ7bC | [
"Yuchen Lu",
"Yikang Shen",
"Siyuan Zhou",
"Aaron Courville",
"Joshua B. Tenenbaum",
"Chuang Gan"
] | Poster | null | Many complex real-world tasks are composed of several levels of subtasks. Humans leverage these hierarchical structures to accelerate the learning process and achieve better generalization. In this work, we study the inductive bias and propose Ordered Memory Policy Network (OMPN) to discover subtask hierarchy by learni... | [
"Task Segmentation",
"Hierarchical Imitation Learning",
"Network Inductive Bias"
] | null | 2,633 | 2103.10972 | title_snapshot |
tYxG_OMs9WE | Property Controllable Variational Autoencoder via Invertible Mutual Dependence | https://openreview.net/forum?id=tYxG_OMs9WE | [
"Xiaojie Guo",
"Yuanqi Du",
"Liang Zhao"
] | Poster | null | Deep generative models have made important progress towards modeling complex, high dimensional data via learning latent representations. Their usefulness is nevertheless often limited by a lack of control over the generative process or a poor understanding of the latent representation. To overcome these issues, attenti... | [
"deep generative models",
"interpretable latent representation",
"disentangled representation learning"
] | null | 2,614 | null | null |
m5Qsh0kBQG | Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients | https://openreview.net/forum?id=m5Qsh0kBQG | [
"Brenden K Petersen",
"Mikel Landajuela Larma",
"Terrell N. Mundhenk",
"Claudio Prata Santiago",
"Soo Kyung Kim",
"Joanne Taery Kim"
] | Oral | null | Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of $\textit{symbolic regression}$. Despite recent advances in training neural networks to solve complex tasks, deep learning approaches to symbolic regression are underexplored. ... | [
"symbolic regression",
"reinforcement learning",
"automated machine learning"
] | null | 2,611 | 1912.04871 | title_snapshot |
bhCDO_cEGCz | Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning | https://openreview.net/forum?id=bhCDO_cEGCz | [
"Zhenfang Chen",
"Jiayuan Mao",
"Jiajun Wu",
"Kwan-Yee Kenneth Wong",
"Joshua B. Tenenbaum",
"Chuang Gan"
] | Poster | null | We study the problem of dynamic visual reasoning on raw videos. This is a challenging problem; currently, state-of-the-art models often require dense supervision on physical object properties and events from simulation, which are impractical to obtain in real life. In this paper, we present the Dynamic Concept Learner ... | [
"Concept Learning",
"Neuro-Symbolic Learning",
"Video Reasoning",
"Visual Reasoning"
] | null | 2,607 | 2103.16564 | title_snapshot |
04LZCAxMSco | Learning a Latent Simplex in Input Sparsity Time | https://openreview.net/forum?id=04LZCAxMSco | [
"Ainesh Bakshi",
"Chiranjib Bhattacharyya",
"Ravi Kannan",
"David Woodruff",
"Samson Zhou"
] | Spotlight | null | We consider the problem of learning a latent $k$-vertex simplex $K\in\mathbb{R}^d$, given $\mathbf{A}\in\mathbb{R}^{d\times n}$, which can be viewed as $n$ data points that are formed by randomly perturbing some latent points in $K$, possibly beyond $K$. A large class of latent variable models, such as adversarial clus... | [
"Latent Simplex",
"numerical linear algebra",
"low-rank approximation"
] | null | 2,604 | 2105.08005 | title_snapshot |
c_E8kFWfhp0 | gradSim: Differentiable simulation for system identification and visuomotor control | https://openreview.net/forum?id=c_E8kFWfhp0 | [
"Krishna Murthy Jatavallabhula",
"Miles Macklin",
"Florian Golemo",
"Vikram Voleti",
"Linda Petrini",
"Martin Weiss",
"Breandan Considine",
"Jérôme Parent-Lévesque",
"Kevin Xie",
"Kenny Erleben",
"Liam Paull",
"Florian Shkurti",
"Derek Nowrouzezahrai",
"Sanja Fidler"
] | Poster | null | In this paper, we tackle the problem of estimating object physical properties such as mass, friction, and elasticity directly from video sequences. Such a system identification problem is fundamentally ill-posed due to the loss of information during image formation. Current best solutions to the problem require precise... | [
"Differentiable simulation",
"System identification",
"Physical parameter estimation",
"3D scene understanding",
"3D vision",
"Differentiable rendering",
"Differentiable physics"
] | null | 2,601 | 2104.02646 | title_snapshot |
RmcPm9m3tnk | Generative Scene Graph Networks | https://openreview.net/forum?id=RmcPm9m3tnk | [
"Fei Deng",
"Zhuo Zhi",
"Donghun Lee",
"Sungjin Ahn"
] | Poster | null | Human perception excels at building compositional hierarchies of parts and objects from unlabeled scenes that help systematic generalization. Yet most work on generative scene modeling either ignores the part-whole relationship or assumes access to predefined part labels. In this paper, we propose Generative Scene Grap... | [
"object-centric representations",
"generative modeling",
"scene generation",
"variational autoencoders"
] | null | 2,593 | null | null |
_kxlwvhOodK | Decentralized Attribution of Generative Models | https://openreview.net/forum?id=_kxlwvhOodK | [
"Changhoon Kim",
"Yi Ren",
"Yezhou Yang"
] | Poster | null | Growing applications of generative models have led to new threats such as malicious personation and digital copyright infringement.
One solution to these threats is model attribution, i.e., the identification of user-end models where the contents under question are generated.
Existing studies showed empirical feasibil... | [
"GANs",
"Generative Model",
"Deepfake",
"Model Attribution"
] | null | 2,588 | 2010.13974 | title_snapshot |
rJA5Pz7lHKb | Improved Autoregressive Modeling with Distribution Smoothing | https://openreview.net/forum?id=rJA5Pz7lHKb | [
"Chenlin Meng",
"Jiaming Song",
"Yang Song",
"Shengjia Zhao",
"Stefano Ermon"
] | Oral | null | While autoregressive models excel at image compression, their sample quality is often lacking. Although not realistic, generated images often have high likelihood according to the model, resembling the case of adversarial examples. Inspired by a successful adversarial defense method, we incorporate randomized smoothing... | [
"generative models",
"autoregressive models"
] | null | 2,584 | 2103.15089 | title_snapshot |
87ZwsaQNHPZ | CPT: Efficient Deep Neural Network Training via Cyclic Precision | https://openreview.net/forum?id=87ZwsaQNHPZ | [
"Yonggan Fu",
"Han Guo",
"Meng Li",
"Xin Yang",
"Yining Ding",
"Vikas Chandra",
"Yingyan Lin"
] | Spotlight | null | Low-precision deep neural network (DNN) training has gained tremendous attention as reducing precision is one of the most effective knobs for boosting DNNs' training time/energy efficiency. In this paper, we attempt to explore low-precision training from a new perspective as inspired by recent findings in understanding... | [
"Efficient training",
"low precision training"
] | null | 2,578 | 2101.09868 | title_snapshot |
71zCSP_HuBN | Individually Fair Rankings | https://openreview.net/forum?id=71zCSP_HuBN | [
"Amanda Bower",
"Hamid Eftekhari",
"Mikhail Yurochkin",
"Yuekai Sun"
] | Poster | null | We develop an algorithm to train individually fair learning-to-rank (LTR) models. The proposed approach ensures items from minority groups appear alongside similar items from majority groups. This notion of fair ranking is based on the definition of individual fairness from supervised learning and is more nuanced than ... | [
"algorithmic fairness",
"learning to rank",
"optimal transport"
] | null | 2,577 | null | null |
w_7JMpGZRh0 | Watch-And-Help: A Challenge for Social Perception and Human-AI Collaboration | https://openreview.net/forum?id=w_7JMpGZRh0 | [
"Xavier Puig",
"Tianmin Shu",
"Shuang Li",
"Zilin Wang",
"Yuan-Hong Liao",
"Joshua B. Tenenbaum",
"Sanja Fidler",
"Antonio Torralba"
] | Spotlight | null | In this paper, we introduce Watch-And-Help (WAH), a challenge for testing social intelligence in agents. In WAH, an AI agent needs to help a human-like agent perform a complex household task efficiently. To succeed, the AI agent needs to i) understand the underlying goal of the task by watching a single demonstration o... | [
"social perception",
"human-AI collaboration",
"theory of mind",
"multi-agent platform",
"virtual environment"
] | null | 2,576 | 2010.09890 | title_snapshot |
LkFG3lB13U5 | Adaptive Federated Optimization | https://openreview.net/forum?id=LkFG3lB13U5 | [
"Sashank J. Reddi",
"Zachary Charles",
"Manzil Zaheer",
"Zachary Garrett",
"Keith Rush",
"Jakub Konečný",
"Sanjiv Kumar",
"Hugh Brendan McMahan"
] | Poster | null | Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. Standard federated optimization methods such as Federated Averaging (FedAvg) are often difficult to tune and exhibit unfavorable con... | [
"Federated learning",
"optimization",
"adaptive optimization",
"distributed optimization"
] | null | 2,569 | 2003.00295 | title_snapshot |
1AoMhc_9jER | GANs Can Play Lottery Tickets Too | https://openreview.net/forum?id=1AoMhc_9jER | [
"Xuxi Chen",
"Zhenyu Zhang",
"Yongduo Sui",
"Tianlong Chen"
] | Poster | null | Deep generative adversarial networks (GANs) have gained growing popularity in numerous scenarios, while usually suffer from high parameter complexities for resource-constrained real-world applications. However, the compression of GANs has less been explored. A few works show that heuristically applying compression tech... | [
"lottery tickets",
"GAN compression",
"generative adversarial networks"
] | null | 2,565 | 2106.00134 | title_snapshot |
PxTIG12RRHS | Score-Based Generative Modeling through Stochastic Differential Equations | https://openreview.net/forum?id=PxTIG12RRHS | [
"Yang Song",
"Jascha Sohl-Dickstein",
"Diederik P Kingma",
"Abhishek Kumar",
"Stefano Ermon",
"Ben Poole"
] | Oral | null | Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reverse-time SDE that transforms the prior distribution... | [
"generative models",
"score-based generative models",
"stochastic differential equations",
"score matching",
"diffusion"
] | null | 2,561 | 2011.13456 | title_snapshot |
DiQD7FWL233 | Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein | https://openreview.net/forum?id=DiQD7FWL233 | [
"Khai Nguyen",
"Son Nguyen",
"Nhat Ho",
"Tung Pham",
"Hung Bui"
] | Poster | null | Relational regularized autoencoder (RAE) is a framework to learn the distribution of data by minimizing a reconstruction loss together with a relational regularization on the prior of latent space. A recent attempt to reduce the inner discrepancy between the prior and aggregated posterior distributions is to incorporat... | [
"Relational regularized autoencoder",
"deep generative model",
"sliced fused Gromov Wasserstein",
"spherical distributions"
] | null | 2,560 | 2010.01787 | title_snapshot |
hPWj1qduVw8 | Learning Reasoning Paths over Semantic Graphs for Video-grounded Dialogues | https://openreview.net/forum?id=hPWj1qduVw8 | [
"Hung Le",
"Nancy F. Chen",
"Steven Hoi"
] | Poster | null | Compared to traditional visual question answering, video-grounded dialogues require additional reasoning over dialogue context to answer questions in a multi-turn setting. Previous approaches to video-grounded dialogues mostly use dialogue context as a simple text input without modelling the inherent information flows ... | [
"video-grounded dialogues",
"reasoning paths",
"semantic graphs"
] | null | 2,558 | 2103.00820 | title_snapshot |
Z4R1vxLbRLO | Extreme Memorization via Scale of Initialization | https://openreview.net/forum?id=Z4R1vxLbRLO | [
"Harsh Mehta",
"Ashok Cutkosky",
"Behnam Neyshabur"
] | Poster | null | We construct an experimental setup in which changing the scale of initialization strongly impacts the implicit regularization induced by SGD, interpolating from good generalization performance to completely memorizing the training set while making little progress on the test set. Moreover, we find that the extent and m... | [
"Scale of initialization",
"Memorization",
"Overfitting",
"Generalization",
"Generalization Measure",
"Understanding Deep Learning"
] | null | 2,552 | 2008.13363 | title_snapshot |
4RbdgBh9gE | Teaching with Commentaries | https://openreview.net/forum?id=4RbdgBh9gE | [
"Aniruddh Raghu",
"Maithra Raghu",
"Simon Kornblith",
"David Duvenaud",
"Geoffrey Hinton"
] | Poster | null | Effective training of deep neural networks can be challenging, and there remain many open questions on how to best learn these models. Recently developed methods to improve neural network training examine teaching: providing learned information during the training process to improve downstream model performance. In thi... | [
"learning to teach",
"metalearning",
"hypergradients"
] | null | 2,547 | 2011.03037 | title_snapshot |
F1vEjWK-lH_ | Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models | https://openreview.net/forum?id=F1vEjWK-lH_ | [
"Zirui Wang",
"Yulia Tsvetkov",
"Orhan Firat",
"Yuan Cao"
] | Spotlight | null | Massively multilingual models subsuming tens or even hundreds of languages pose great challenges to multi-task optimization. While it is a common practice to apply a language-agnostic procedure optimizing a joint multilingual task objective, how to properly characterize and take advantage of its underlying problem stru... | [
"Multi-task Learning",
"Multilingual Modeling"
] | null | 2,546 | 2010.05874 | title_snapshot |
xCcdBRQEDW | PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics | https://openreview.net/forum?id=xCcdBRQEDW | [
"Zhiao Huang",
"Yuanming Hu",
"Tao Du",
"Siyuan Zhou",
"Hao Su",
"Joshua B. Tenenbaum",
"Chuang Gan"
] | Spotlight | null | Simulated virtual environments serve as one of the main driving forces behind developing and evaluating skill learning algorithms. However, existing environments typically only simulate rigid body physics. Additionally, the simulation process usually does not provide gradients that might be useful for planning and cont... | [
"Soft Body",
"Differentiable Physics",
"Benchmark"
] | null | 2,543 | 2104.03311 | title_snapshot |
-ODN6SbiUU | In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning | https://openreview.net/forum?id=-ODN6SbiUU | [
"Mamshad Nayeem Rizve",
"Kevin Duarte",
"Yogesh S Rawat",
"Mubarak Shah"
] | Poster | null | The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance. However, they heavily rely on domain-specific data augmentations, which are not easy to generate for all data modalities. Pseudo-labeling (PL) is a general SSL approach... | [
"Semi-Supervised Learning",
"Pseudo-Labeling",
"Uncertainty",
"Calibration",
"Deep Learning"
] | null | 2,541 | 2101.06329 | title_snapshot |
MDsQkFP1Aw | Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds | https://openreview.net/forum?id=MDsQkFP1Aw | [
"Efthymios Tzinis",
"Scott Wisdom",
"Aren Jansen",
"Shawn Hershey",
"Tal Remez",
"Dan Ellis",
"John R. Hershey"
] | Poster | null | Recent progress in deep learning has enabled many advances in sound separation and visual scene understanding. However, extracting sound sources which are apparent in natural videos remains an open problem. In this work, we present AudioScope, a novel audio-visual sound separation framework that can be trained without ... | [
"Audio-visual sound separation",
"in-the-wild data",
"unsupervised learning",
"self-supervised learning",
"universal sound separation"
] | null | 2,533 | 2011.01143 | title_snapshot |
bjkX6Kzb5H | Cut out the annotator, keep the cutout: better segmentation with weak supervision | https://openreview.net/forum?id=bjkX6Kzb5H | [
"Sarah Hooper",
"Michael Wornow",
"Ying Hang Seah",
"Peter Kellman",
"Hui Xue",
"Frederic Sala",
"Curtis Langlotz",
"Christopher Re"
] | Poster | null | Constructing large, labeled training datasets for segmentation models is an expensive and labor-intensive process. This is a common challenge in machine learning, addressed by methods that require few or no labeled data points such as few-shot learning (FSL) and weakly-supervised learning (WS). Such techniques, however... | [
"Weak supervision",
"segmentation",
"CNN",
"latent variable",
"medical imaging"
] | null | 2,529 | null | null |
Ozk9MrX1hvA | CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding | https://openreview.net/forum?id=Ozk9MrX1hvA | [
"Yanru Qu",
"Dinghan Shen",
"Yelong Shen",
"Sandra Sajeev",
"Weizhu Chen",
"Jiawei Han"
] | Poster | null | Data augmentation has been demonstrated as an effective strategy for improving model generalization and data efficiency. However, due to the discrete nature of natural language, designing label-preserving transformations for text data tends to be more challenging. In this paper, we propose a novel data augmentation fr... | [
"data augmentation",
"natural language understanding",
"consistency training",
"contrastive learning"
] | null | 2,524 | 2010.08670 | title_snapshot |
KvyxFqZS_D | Global Convergence of Three-layer Neural Networks in the Mean Field Regime | https://openreview.net/forum?id=KvyxFqZS_D | [
"Huy Tuan Pham",
"Phan-Minh Nguyen"
] | Oral | null | In the mean field regime, neural networks are appropriately scaled so that as the width tends to infinity, the learning dynamics tends to a nonlinear and nontrivial dynamical limit, known as the mean field limit. This lends a way to study large-width neural networks via analyzing the mean field limit. Recent works have... | [
"deep learning theory"
] | null | 2,523 | 2105.05228 | title_snapshot |
EQfpYwF3-b | Deep Learning meets Projective Clustering | https://openreview.net/forum?id=EQfpYwF3-b | [
"Alaa Maalouf",
"Harry Lang",
"Daniela Rus",
"Dan Feldman"
] | Poster | null | A common approach for compressing Natural Language Processing (NLP) networks is to encode the embedding layer as a matrix $A\in\mathbb{R}^{n\times d}$, compute its rank-$j$ approximation $A_j$ via SVD (Singular Value Decomposition), and then factor $A_j$ into a pair of matrices that correspond to smaller fully-connecte... | [
"Compressing Deep Networks",
"NLP",
"Matrix Factorization",
"SVD"
] | null | 2,517 | 2010.04290 | title_snapshot |
b7g3_ZMHnT0 | Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation | https://openreview.net/forum?id=b7g3_ZMHnT0 | [
"Mrigank Raman",
"Aaron Chan",
"Siddhant Agarwal",
"PeiFeng Wang",
"Hansen Wang",
"Sungchul Kim",
"Ryan Rossi",
"Handong Zhao",
"Nedim Lipka",
"Xiang Ren"
] | Poster | null | Knowledge graphs (KGs) have helped neural models improve performance on various knowledge-intensive tasks, like question answering and item recommendation. By using attention over the KG, such KG-augmented models can also "explain" which KG information was most relevant for making a given prediction. In this paper, we ... | [
"neural symbolic reasoning",
"interpretability",
"model explanation",
"faithfulness",
"knowledge graph",
"commonsense question answering",
"recommender system"
] | null | 2,516 | 2010.12872 | title_snapshot |
m4UCf24r0Y | Knowledge Distillation as Semiparametric Inference | https://openreview.net/forum?id=m4UCf24r0Y | [
"Tri Dao",
"Govinda M Kamath",
"Vasilis Syrgkanis",
"Lester Mackey"
] | Poster | null | A popular approach to model compression is to train an inexpensive student model to mimic the class probabilities of a highly accurate but cumbersome teacher model. Surprisingly, this two-step knowledge distillation process often leads to higher accuracy than training the student directly on labeled data. To explain an... | [
"knowledge distillation",
"semiparametric inference",
"generalization bounds",
"model compression",
"cross-fitting",
"orthogonal machine learning",
"loss correction"
] | null | 2,515 | 2104.09732 | title_snapshot |
Ti87Pv5Oc8 | Meta-Learning with Neural Tangent Kernels | https://openreview.net/forum?id=Ti87Pv5Oc8 | [
"Yufan Zhou",
"Zhenyi Wang",
"Jiayi Xian",
"Changyou Chen",
"Jinhui Xu"
] | Poster | null | Model Agnostic Meta-Learning (MAML) has emerged as a standard framework for meta-learning, where a meta-model is learned with the ability of fast adapting to new tasks. However, as a double-looped optimization problem, MAML needs to differentiate through the whole inner-loop optimization path for every outer-loop train... | [
"meta-learning",
"neural tangent kernel"
] | null | 2,513 | 2102.03909 | title_snapshot |
9r30XCjf5Dt | Vulnerability-Aware Poisoning Mechanism for Online RL with Unknown Dynamics | https://openreview.net/forum?id=9r30XCjf5Dt | [
"Yanchao Sun",
"Da Huo",
"Furong Huang"
] | Poster | null | Poisoning attacks on Reinforcement Learning (RL) systems could take advantage of RL algorithm’s vulnerabilities and cause failure of the learning. However, prior works on poisoning RL usually either unrealistically assume the attacker knows the underlying Markov Decision Process (MDP), or directly apply the poisoning m... | [
"poisoning attack",
"policy gradient",
"vulnerability of RL",
"deep RL"
] | null | 2,512 | 2009.00774 | title_snapshot |
ZTFeSBIX9C | Understanding and Improving Lexical Choice in Non-Autoregressive Translation | https://openreview.net/forum?id=ZTFeSBIX9C | [
"Liang Ding",
"Longyue Wang",
"Xuebo Liu",
"Derek F. Wong",
"Dacheng Tao",
"Zhaopeng Tu"
] | Poster | null | Knowledge distillation (KD) is essential for training non-autoregressive translation (NAT) models by reducing the complexity of the raw data with an autoregressive teacher model. In this study, we empirically show that as a side effect of this training, the lexical choice errors on low-frequency words are propagated to... | [] | null | 2,511 | 2012.14583 | title_snapshot |
PKubaeJkw3 | Rethinking Architecture Selection in Differentiable NAS | https://openreview.net/forum?id=PKubaeJkw3 | [
"Ruochen Wang",
"Minhao Cheng",
"Xiangning Chen",
"Xiaocheng Tang",
"Cho-Jui Hsieh"
] | Oral | null | Differentiable Neural Architecture Search is one of the most popular Neural Architecture Search (NAS) methods for its search efficiency and simplicity, accomplished by jointly optimizing the model weight and architecture parameters in a weight-sharing supernet via gradient-based algorithms. At the end of the search pha... | [] | null | 2,508 | 2108.04392 | title_snapshot |
QYjO70ACDK | Distributional Sliced-Wasserstein and Applications to Generative Modeling | https://openreview.net/forum?id=QYjO70ACDK | [
"Khai Nguyen",
"Nhat Ho",
"Tung Pham",
"Hung Bui"
] | Spotlight | null | Sliced-Wasserstein distance (SW) and its variant, Max Sliced-Wasserstein distance (Max-SW), have been used widely in the recent years due to their fast computation and scalability even when the probability measures lie in a very high dimensional space. However, SW requires many unnecessary projection samples to approxi... | [
"Deep generative models",
"Sliced Wasserstein",
"Optimal Transport"
] | null | 2,506 | 2002.07367 | title_snapshot |
0XXpJ4OtjW | Evolving Reinforcement Learning Algorithms | https://openreview.net/forum?id=0XXpJ4OtjW | [
"John D Co-Reyes",
"Yingjie Miao",
"Daiyi Peng",
"Esteban Real",
"Quoc V Le",
"Sergey Levine",
"Honglak Lee",
"Aleksandra Faust"
] | Oral | null | We propose a method for meta-learning reinforcement learning algorithms by searching over the space of computational graphs which compute the loss function for a value-based model-free RL agent to optimize. The learned algorithms are domain-agnostic and can generalize to new environments not seen during training. Our m... | [
"reinforcement learning",
"evolutionary algorithms",
"meta-learning",
"genetic programming"
] | null | 2,502 | 2101.03958 | title_snapshot |
b9PoimzZFJ | Systematic generalisation with group invariant predictions | https://openreview.net/forum?id=b9PoimzZFJ | [
"Faruk Ahmed",
"Yoshua Bengio",
"Harm van Seijen",
"Aaron Courville"
] | Spotlight | null | We consider situations where the presence of dominant simpler correlations with the target variable in a training set can cause an SGD-trained neural network to be less reliant on more persistently correlating complex features. When the non-persistent, simpler correlations correspond to non-semantic background factors,... | [
"Systematic generalisation",
"invariance penalty",
"semantic anomaly detection"
] | null | 2,500 | null | null |
H6ATjJ0TKdf | Layer-adaptive Sparsity for the Magnitude-based Pruning | https://openreview.net/forum?id=H6ATjJ0TKdf | [
"Jaeho Lee",
"Sejun Park",
"Sangwoo Mo",
"Sungsoo Ahn",
"Jinwoo Shin"
] | Poster | null | Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sparsity and performance. However, without a clear consensus on ``how to choose,'' the layerwise sparsities are mostly selected algorithm-by-a... | [
"network pruning",
"layerwise sparsity",
"magnitude-based pruning"
] | null | 2,494 | 2010.07611 | title_snapshot |
n1HD8M6WGn | Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning | https://openreview.net/forum?id=n1HD8M6WGn | [
"Xuebo Liu",
"Longyue Wang",
"Derek F. Wong",
"Liang Ding",
"Lidia S. Chao",
"Zhaopeng Tu"
] | Poster | null | Encoder layer fusion (EncoderFusion) is a technique to fuse all the encoder layers (instead of the uppermost layer) for sequence-to-sequence (Seq2Seq) models, which has proven effective on various NLP tasks. However, it is still not entirely clear why and when EncoderFusion should work. In this paper, our main contribu... | [
"Encoder layer fusion",
"Transformer",
"Sequence-to-sequence learning",
"Machine translation",
"Summarization",
"Grammatical error correction"
] | null | 2,488 | 2012.14768 | title_snapshot |
-M0QkvBGTTq | SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization | https://openreview.net/forum?id=-M0QkvBGTTq | [
"A F M Shahab Uddin",
"Mst. Sirazam Monira",
"Wheemyung Shin",
"TaeChoong Chung",
"Sung-Ho Bae"
] | Poster | null | Advanced data augmentation strategies have widely been studied to improve the generalization ability of deep learning models. Regional dropout is one of the popular solutions that guides the model to focus on less discriminative parts by randomly removing image regions, resulting in improved regularization. However, su... | [
"SaliencyMix",
"Saliency Guided Data Augmentation",
"Data Augmentation",
"Regularization"
] | null | 2,486 | 2006.01791 | title_snapshot |
yWkP7JuHX1 | Image GANs meet Differentiable Rendering for Inverse Graphics and Interpretable 3D Neural Rendering | https://openreview.net/forum?id=yWkP7JuHX1 | [
"Yuxuan Zhang",
"Wenzheng Chen",
"Huan Ling",
"Jun Gao",
"Yinan Zhang",
"Antonio Torralba",
"Sanja Fidler"
] | Oral | null | Differentiable rendering has paved the way to training neural networks to perform “inverse graphics” tasks such as predicting 3D geometry from monocular photographs. To train high performing models, most of the current approaches rely on multi-view imagery which are not readily available in practice. Recent Generative... | [
"Differentiable rendering",
"inverse graphics",
"GANs"
] | null | 2,477 | 2010.09125 | title_snapshot |
_zx8Oka09eF | Are wider nets better given the same number of parameters? | https://openreview.net/forum?id=_zx8Oka09eF | [
"Anna Golubeva",
"Guy Gur-Ari",
"Behnam Neyshabur"
] | Poster | null | Empirical studies demonstrate that the performance of neural networks improves with increasing number of parameters. In most of these studies, the number of parameters is increased by increasing the network width. This begs the question: Is the observed improvement due to the larger number of parameters, or is it due t... | [
"network width",
"over-parametrization",
"understanding deep learning"
] | null | 2,468 | 2010.14495 | title_snapshot |
jP1vTH3inC | Discovering Non-monotonic Autoregressive Orderings with Variational Inference | https://openreview.net/forum?id=jP1vTH3inC | [
"Xuanlin Li",
"Brandon Trabucco",
"Dong Huk Park",
"Michael Luo",
"Sheng Shen",
"Trevor Darrell",
"Yang Gao"
] | Poster | null | The predominant approach for language modeling is to encode a sequence of tokens from left to right, but this eliminates a source of information: the order by which the sequence was naturally generated. One strategy to recover this information is to decode both the content and ordering of tokens. Some prior work superv... | [
"variational inference",
"unsupervised learning",
"computer vision",
"natural language processing",
"optimization",
"reinforcement learning"
] | null | 2,461 | 2110.15797 | title_snapshot |
IgIk8RRT-Z | CompOFA – Compound Once-For-All Networks for Faster Multi-Platform Deployment | https://openreview.net/forum?id=IgIk8RRT-Z | [
"Manas Sahni",
"Shreya Varshini",
"Alind Khare",
"Alexey Tumanov"
] | Poster | null | The emergence of CNNs in mainstream deployment has necessitated methods to design and train efficient architectures tailored to maximize the accuracy under diverse hardware and latency constraints. To scale these resource-intensive tasks with an increasing number of deployment targets, Once-For-All (OFA) proposed an ap... | [
"Efficient Deep Learning",
"Latency-aware Neural Architecture Search",
"AutoML"
] | null | 2,447 | 2104.12642 | title_snapshot |
mCtadqIxOJ | Representing Partial Programs with Blended Abstract Semantics | https://openreview.net/forum?id=mCtadqIxOJ | [
"Maxwell Nye",
"Yewen Pu",
"Matthew Bowers",
"Jacob Andreas",
"Joshua B. Tenenbaum",
"Armando Solar-Lezama"
] | Poster | null | Synthesizing programs from examples requires searching over a vast, combinatorial space of possible programs. In this search process, a key challenge is representing the behavior of a partially written program before it can be executed, to judge if it is on the right track and predict where to search next. We introduce... | [
"program synthesis",
"representation learning",
"abstract interpretation",
"modular neural networks"
] | null | 2,437 | 2012.12964 | title_snapshot |
wWK7yXkULyh | MONGOOSE: A Learnable LSH Framework for Efficient Neural Network Training | https://openreview.net/forum?id=wWK7yXkULyh | [
"Beidi Chen",
"Zichang Liu",
"Binghui Peng",
"Zhaozhuo Xu",
"Jonathan Lingjie Li",
"Tri Dao",
"Zhao Song",
"Anshumali Shrivastava",
"Christopher Re"
] | Oral | null | Recent advances by practitioners in the deep learning community have breathed new life into Locality Sensitive Hashing (LSH), using it to reduce memory and time bottlenecks in neural network (NN) training. However, while LSH has sub-linear guarantees for approximate near-neighbor search in theory, it is known to have i... | [
"Large-scale Deep Learning",
"Large-scale Machine Learning",
"Efficient Training",
"Randomized Algorithms"
] | null | 2,434 | null | null |
TYXs_y84xRj | PolarNet: Learning to Optimize Polar Keypoints for Keypoint Based Object Detection | https://openreview.net/forum?id=TYXs_y84xRj | [
"Wu Xiongwei",
"Doyen Sahoo",
"Steven HOI"
] | Poster | null | A variety of anchor-free object detectors have been actively proposed as possible alternatives to the mainstream anchor-based detectors that often rely on complicated design of anchor boxes. Despite achieving promising performance on par with anchor-based detectors, the existing anchor-free detectors such as FCOS or Ce... | [
"Object Detection",
"Deep Learning"
] | null | 2,432 | null | null |
Cnon5ezMHtu | Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective | https://openreview.net/forum?id=Cnon5ezMHtu | [
"Wuyang Chen",
"Xinyu Gong",
"Zhangyang Wang"
] | Poster | null | Neural Architecture Search (NAS) has been explosively studied to automate the discovery of top-performer neural networks. Current works require heavy training of supernet or intensive architecture evaluations, thus suffering from heavy resource consumption and often incurring search bias due to truncated training or ap... | [
"Neural Architecture Search",
"neural tangent kernel",
"number of linear regions"
] | null | 2,426 | 2102.11535 | title_snapshot |
8qDwejCuCN | Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding | https://openreview.net/forum?id=8qDwejCuCN | [
"Sana Tonekaboni",
"Danny Eytan",
"Anna Goldenberg"
] | Poster | null | Time series are often complex and rich in information but sparsely labeled and therefore challenging to model. In this paper, we propose a self-supervised framework for learning robust and generalizable representations for time series. Our approach, called Temporal Neighborhood Coding (TNC), takes advantage of the loca... | [] | null | 2,425 | 2106.00750 | title_snapshot |
KJNcAkY8tY4 | Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth | https://openreview.net/forum?id=KJNcAkY8tY4 | [
"Thao Nguyen",
"Maithra Raghu",
"Simon Kornblith"
] | Poster | null | A key factor in the success of deep neural networks is the ability to scale models to improve performance by varying the architecture depth and width. This simple property of neural network design has resulted in highly effective architectures for a variety of tasks. Nevertheless, there is limited understanding of effe... | [
"Representation learning"
] | null | 2,417 | 2010.15327 | title_snapshot |
cu7IUiOhujH | Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning | https://openreview.net/forum?id=cu7IUiOhujH | [
"Beliz Gunel",
"Jingfei Du",
"Alexis Conneau",
"Veselin Stoyanov"
] | Poster | null | State-of-the-art natural language understanding classification models follow two-stages: pre-training a large language model on an auxiliary task, and then fine-tuning the model on a task-specific labeled dataset using cross-entropy loss. However, the cross-entropy loss has several shortcomings that can lead to sub-opt... | [
"pre-trained language model fine-tuning",
"supervised contrastive learning",
"natural language understanding",
"few-shot learning",
"robustness",
"generalization"
] | null | 2,415 | 2011.01403 | title_snapshot |
bnY0jm4l59 | Memory Optimization for Deep Networks | https://openreview.net/forum?id=bnY0jm4l59 | [
"Aashaka Shah",
"Chao-Yuan Wu",
"Jayashree Mohan",
"Vijay Chidambaram",
"Philipp Kraehenbuehl"
] | Spotlight | null | Deep learning is slowly, but steadily, hitting a memory bottleneck. While the tensor computation in top-of-the-line GPUs increased by $32\times$ over the last five years, the total available memory only grew by $2.5\times$. This prevents researchers from exploring larger architectures, as training large networks requir... | [
"memory optimized training",
"memory efficient training",
"checkpointing",
"deep network training"
] | null | 2,408 | 2010.14501 | title_snapshot |
Ysuv-WOFeKR | Parrot: Data-Driven Behavioral Priors for Reinforcement Learning | https://openreview.net/forum?id=Ysuv-WOFeKR | [
"Avi Singh",
"Huihan Liu",
"Gaoyue Zhou",
"Albert Yu",
"Nicholas Rhinehart",
"Sergey Levine"
] | Oral | null | Reinforcement learning provides a general framework for flexible decision making and control, but requires extensive data collection for each new task that an agent needs to learn. In other machine learning fields, such as natural language processing or computer vision, pre-training on large, previously collected datas... | [
"reinforcement learning",
"imitation learning"
] | null | 2,407 | 2011.10024 | title_snapshot |
tlV90jvZbw | Early Stopping in Deep Networks: Double Descent and How to Eliminate it | https://openreview.net/forum?id=tlV90jvZbw | [
"Reinhard Heckel",
"Fatih Furkan Yilmaz"
] | Poster | null | Over-parameterized models, such as large deep networks, often exhibit a double descent phenomenon, whereas a function of model size, error first decreases, increases, and decreases at last. This intriguing double descent behavior also occurs as a function of training epochs and has been conjectured to arise because tra... | [
"early stopping",
"double descent"
] | null | 2,406 | 2007.10099 | title_snapshot |
F8whUO8HNbP | Contrastive Syn-to-Real Generalization | https://openreview.net/forum?id=F8whUO8HNbP | [
"Wuyang Chen",
"Zhiding Yu",
"Shalini De Mello",
"Sifei Liu",
"Jose M. Alvarez",
"Zhangyang Wang",
"Anima Anandkumar"
] | Poster | null | Training on synthetic data can be beneficial for label or data-scarce scenarios. However, synthetically trained models often suffer from poor generalization in real domains due to domain gaps. In this work, we make a key observation that the diversity of the learned feature embeddings plays an important role in the gen... | [
"synthetic-to-real generalization",
"domain generalization"
] | null | 2,403 | 2104.02290 | title_snapshot |
kWSeGEeHvF8 | Benchmarks for Deep Off-Policy Evaluation | https://openreview.net/forum?id=kWSeGEeHvF8 | [
"Justin Fu",
"Mohammad Norouzi",
"Ofir Nachum",
"George Tucker",
"ziyu wang",
"Alexander Novikov",
"Mengjiao Yang",
"Michael R Zhang",
"Yutian Chen",
"Aviral Kumar",
"Cosmin Paduraru",
"Sergey Levine",
"Thomas Paine"
] | Poster | null | Off-policy evaluation (OPE) holds the promise of being able to leverage large, offline datasets for both evaluating and selecting complex policies for decision making. The ability to learn offline is particularly important in many real-world domains, such as in healthcare, recommender systems, or robotics, where online... | [
"reinforcement learning",
"off-policy evaluation",
"benchmarks"
] | null | 2,401 | 2103.16596 | title_snapshot |
3k20LAiHYL2 | Pre-training Text-to-Text Transformers for Concept-centric Common Sense | https://openreview.net/forum?id=3k20LAiHYL2 | [
"Wangchunshu Zhou",
"Dong-Ho Lee",
"Ravi Kiran Selvam",
"Seyeon Lee",
"Xiang Ren"
] | Poster | null | Pretrained language models (PTLM) have achieved impressive results in a range of natural language understanding (NLU) and generation (NLG) tasks that require a syntactic and semantic understanding of the text. However, current pre-training objectives such as masked token prediction (for BERT-style PTLMs) and masked spa... | [
"Language Model Pre-training",
"Commonsense Reasoning",
"Self-supervised Learning"
] | null | 2,398 | 2011.07956 | title_snapshot |
8E1-f3VhX1o | Combining Label Propagation and Simple Models out-performs Graph Neural Networks | https://openreview.net/forum?id=8E1-f3VhX1o | [
"Qian Huang",
"Horace He",
"Abhay Singh",
"Ser-Nam Lim",
"Austin Benson"
] | Poster | null | Graph Neural Networks (GNNs) are a predominant technique for learning over graphs. However, there is relatively little understanding of why GNNs are successful in practice and whether they are necessary for good performance. Here, we show that for many standard transductive node classification benchmarks, we can exceed... | [
"graphs",
"graph neural networks",
"label propagation",
"simple",
"residual"
] | null | 2,396 | 2010.13993 | title_snapshot |
_X_4Akcd8Re | Learning Long-term Visual Dynamics with Region Proposal Interaction Networks | https://openreview.net/forum?id=_X_4Akcd8Re | [
"Haozhi Qi",
"Xiaolong Wang",
"Deepak Pathak",
"Yi Ma",
"Jitendra Malik"
] | Poster | null | Learning long-term dynamics models is the key to understanding physical common sense. Most existing approaches on learning dynamics from visual input sidestep long-term predictions by resorting to rapid re-planning with short-term models. This not only requires such models to be super accurate but also limits them only... | [
"dynamics prediction",
"interaction networks",
"physical reasoning"
] | null | 2,388 | 2008.02265 | title_snapshot |
a3wKPZpGtCF | Chaos of Learning Beyond Zero-sum and Coordination via Game Decompositions | https://openreview.net/forum?id=a3wKPZpGtCF | [
"Yun Kuen Cheung",
"Yixin Tao"
] | Poster | null | It is of primary interest for ML to understand how agents learn and interact dynamically in competitive environments and games (e.g. GANs). But this has been a difficult task, as irregular behaviors are commonly observed in such systems. This can be explained theoretically, for instance, by the works of Cheung and Pili... | [
"Learning in Games",
"Lyapunov Chaos",
"Game Decomposition",
"Multiplicative Weights Update",
"Follow-the-Regularized-Leader",
"Volume Analysis",
"Dynamical Systems"
] | null | 2,387 | 2008.00540 | title_snapshot |
dgd4EJqsbW5 | Control-Aware Representations for Model-based Reinforcement Learning | https://openreview.net/forum?id=dgd4EJqsbW5 | [
"Brandon Cui",
"Yinlam Chow",
"Mohammad Ghavamzadeh"
] | Poster | null | A major challenge in modern reinforcement learning (RL) is efficient control of dynamical systems from high-dimensional sensory observations. Learning controllable embedding (LCE) is a promising approach that addresses this challenge by embedding the observations into a lower-dimensional latent space, estimating the ... | [] | null | 2,381 | 2006.13408 | title_snapshot |
sRA5rLNpmQc | Provably robust classification of adversarial examples with detection | https://openreview.net/forum?id=sRA5rLNpmQc | [
"Fatemeh Sheikholeslami",
"Ali Lotfi",
"J Zico Kolter"
] | Poster | null | Adversarial attacks against deep networks can be defended against either by building robust classifiers or, by creating classifiers that can \emph{detect} the presence of adversarial perturbations. Although it may intuitively seem easier to simply detect attacks rather than build a robust classifier, this has not bour... | [
"Adversarial robustness",
"robust deep learning"
] | null | 2,380 | null | null |
_TM6rT7tXke | Return-Based Contrastive Representation Learning for Reinforcement Learning | https://openreview.net/forum?id=_TM6rT7tXke | [
"Guoqing Liu",
"Chuheng Zhang",
"Li Zhao",
"Tao Qin",
"Jinhua Zhu",
"Li Jian",
"Nenghai Yu",
"Tie-Yan Liu"
] | Poster | null | Recently, various auxiliary tasks have been proposed to accelerate representation learning and improve sample efficiency in deep reinforcement learning (RL). However, existing auxiliary tasks do not take the characteristics of RL problems into consideration and are unsupervised. By leveraging returns, the most importan... | [
"reinforcement learning",
"auxiliary task",
"representation learning",
"contrastive learning"
] | null | 2,378 | 2102.10960 | title_snapshot |
ijJZbomCJIm | Adversarially-Trained Deep Nets Transfer Better: Illustration on Image Classification | https://openreview.net/forum?id=ijJZbomCJIm | [
"Francisco Utrera",
"Evan Kravitz",
"N. Benjamin Erichson",
"Rajiv Khanna",
"Michael W. Mahoney"
] | Poster | null | Transfer learning has emerged as a powerful methodology for adapting pre-trained deep neural networks on image recognition tasks to new domains. This process consists of taking a neural network pre-trained on a large feature-rich source dataset, freezing the early layers that encode essential generic image properties, ... | [
"transfer learning",
"adversarial training",
"influence functions",
"limited data"
] | null | 2,366 | 2007.05869 | title_snapshot |
v9hAX77--cZ | Learning Structural Edits via Incremental Tree Transformations | https://openreview.net/forum?id=v9hAX77--cZ | [
"Ziyu Yao",
"Frank F. Xu",
"Pengcheng Yin",
"Huan Sun",
"Graham Neubig"
] | Poster | null | While most neural generative models generate outputs in a single pass, the human creative process is usually one of iterative building and refinement. Recent work has proposed models of editing processes, but these mostly focus on editing sequential data and/or only model a single editing pass. In this paper, we presen... | [
"Tree-structured Data",
"Edit",
"Incremental Tree Transformations",
"Representation Learning",
"Imitation Learning",
"Source Code"
] | null | 2,364 | 2101.12087 | title_snapshot |
hWr3e3r-oH5 | Cross-Attentional Audio-Visual Fusion for Weakly-Supervised Action Localization | https://openreview.net/forum?id=hWr3e3r-oH5 | [
"Jun-Tae Lee",
"Mihir Jain",
"Hyoungwoo Park",
"Sungrack Yun"
] | Poster | null | Temporally localizing actions in videos is one of the key components for video understanding. Learning from weakly-labeled data is seen as a potential solution towards avoiding expensive frame-level annotations. Different from other works which only depend on visual-modality, we propose to learn richer audiovisual repr... | [
"Audio-Visual",
"Multimodal Attention",
"Action localization",
"Event localization",
"Weak-supervision"
] | null | 2,362 | null | null |
BUlyHkzjgmA | Improved Estimation of Concentration Under $\ell_p$-Norm Distance Metrics Using Half Spaces | https://openreview.net/forum?id=BUlyHkzjgmA | [
"Jack Prescott",
"Xiao Zhang",
"David Evans"
] | Poster | null | Concentration of measure has been argued to be the fundamental cause of adversarial vulnerability. Mahloujifar et al. (2019) presented an empirical way to measure the concentration of a data distribution using samples, and employed it to find lower bounds on intrinsic robustness for several benchmark datasets. However,... | [
"Adversarial Examples",
"Concentration of Measure",
"Gaussian Isoperimetric Inequality"
] | null | 2,357 | 2103.12913 | title_snapshot |
MyHwDabUHZm | Beyond Categorical Label Representations for Image Classification | https://openreview.net/forum?id=MyHwDabUHZm | [
"Boyuan Chen",
"Yu Li",
"Sunand Raghupathi",
"Hod Lipson"
] | Poster | null | We find that the way we choose to represent data labels can have a profound effect on the quality of trained models. For example, training an image classifier to regress audio labels rather than traditional categorical probabilities produces a more reliable classification. This result is surprising, considering that au... | [
"Label Representation",
"Image Classification",
"Representation Learning"
] | null | 2,341 | 2104.02226 | title_snapshot |
JCRblSgs34Z | Fantastic Four: Differentiable and Efficient Bounds on Singular Values of Convolution Layers | https://openreview.net/forum?id=JCRblSgs34Z | [
"Sahil Singla",
"Soheil Feizi"
] | Poster | null | In deep neural networks, the spectral norm of the Jacobian of a layer bounds the factor by which the norm of a signal changes during forward/backward propagation. Spectral norm regularizations have been shown to improve generalization, robustness and optimization of deep learning methods. Existing methods to compute th... | [
"spectral regularization",
"spectral normalization"
] | null | 2,340 | 1911.10258 | title_judge |
iOnhIy-a-0n | Accelerating Convergence of Replica Exchange Stochastic Gradient MCMC via Variance Reduction | https://openreview.net/forum?id=iOnhIy-a-0n | [
"Wei Deng",
"Qi Feng",
"Georgios P. Karagiannis",
"Guang Lin",
"Faming Liang"
] | Poster | null | Replica exchange stochastic gradient Langevin dynamics (reSGLD) has shown promise in accelerating the convergence in non-convex learning; however, an excessively large correction for avoiding biases from noisy energy estimators has limited the potential of the acceleration. To address this issue, we study the variance ... | [
"variance reduction",
"replica exchange",
"parallel tempering",
"stochastic gradient Langevin dynamics",
"uncertainty quantification",
"change of measure",
"generalized Girsanov theorem",
"Dirichlet form",
"Markov jump process"
] | null | 2,336 | 2010.01084 | title_snapshot |
Pzj6fzU6wkj | IsarStep: a Benchmark for High-level Mathematical Reasoning | https://openreview.net/forum?id=Pzj6fzU6wkj | [
"Wenda Li",
"Lei Yu",
"Yuhuai Wu",
"Lawrence C. Paulson"
] | Poster | null | A well-defined benchmark is essential for measuring and accelerating research progress of machine learning models. In this paper, we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models. We build a non-synthetic dataset from the largest rep... | [
"mathematical reasoning",
"dataset",
"benchmark",
"reasoning",
"transformer"
] | null | 2,324 | 2006.09265 | title_snapshot |
_0kaDkv3dVf | HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark | https://openreview.net/forum?id=_0kaDkv3dVf | [
"Chaojian Li",
"Zhongzhi Yu",
"Yonggan Fu",
"Yongan Zhang",
"Yang Zhao",
"Haoran You",
"Qixuan Yu",
"Yue Wang",
"Cong Hao",
"Yingyan Lin"
] | Spotlight | null | HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous attention by automating the design of deep neural networks deployed in more resource-constrained daily life devices. Despite its promising performance, developing optimal HW-NAS solutions can be prohibitively challenging as it requires cr... | [
"Hardware-Aware Neural Architecture Search",
"AutoML",
"Benchmark"
] | null | 2,323 | 2103.10584 | title_snapshot |
VVdmjgu7pKM | Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments | https://openreview.net/forum?id=VVdmjgu7pKM | [
"Anirudh Goyal",
"Alex Lamb",
"Phanideep Gampa",
"Philippe Beaudoin",
"Charles Blundell",
"Sergey Levine",
"Yoshua Bengio",
"Michael Curtis Mozer"
] | Poster | null | Modeling a structured, dynamic environment like a video game requires keeping track of the objects and their states (declarative knowledge) as well as predicting how objects behave (procedural knowledge). Black-box models with a monolithic hidden state often fail to apply procedural knowledge consistently and uniformly... | [
"procedural knowledge",
"declarative knowledge",
"Systematicity"
] | null | 2,322 | 2006.16225 | title_judge |
hx1IXFHAw7R | Provable Rich Observation Reinforcement Learning with Combinatorial Latent States | https://openreview.net/forum?id=hx1IXFHAw7R | [
"Dipendra Misra",
"Qinghua Liu",
"Chi Jin",
"John Langford"
] | Poster | null | We propose a novel setting for reinforcement learning that combines two common real-world difficulties: presence of observations (such as camera images) and factored states (such as location of objects). In our setting, the agent receives observations generated stochastically from a "latent" factored state. These obser... | [
"Reinforcement learning theory",
"Rich observation",
"Noise-contrastive learning",
"State abstraction",
"Factored MDP"
] | null | 2,317 | null | null |
hJmtwocEqzc | LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition | https://openreview.net/forum?id=hJmtwocEqzc | [
"Valeriia Cherepanova",
"Micah Goldblum",
"Harrison Foley",
"Shiyuan Duan",
"John P Dickerson",
"Gavin Taylor",
"Tom Goldstein"
] | Poster | null | Facial recognition systems are increasingly deployed by private corporations, government agencies, and contractors for consumer services and mass surveillance programs alike. These systems are typically built by scraping social media profiles for user images. Adversarial perturbations have been proposed for bypassing... | [
"facial recognition",
"adversarial attacks"
] | null | 2,316 | 2101.07922 | title_snapshot |
7t1FcJUWhi3 | Neural Networks for Learning Counterfactual G-Invariances from Single Environments | https://openreview.net/forum?id=7t1FcJUWhi3 | [
"S Chandra Mouli",
"Bruno Ribeiro"
] | Poster | null | Despite —or maybe because of— their astonishing capacity to fit data, neural networks are believed to have difficulties extrapolating beyond training data distribution. This work shows that, for extrapolations based on finite transformation groups, a model’s inability to extrapolate is unrelated to its capacity. Rather... | [
"Extrapolation",
"G-invariance regularization",
"Counterfactual inference",
"Invariant subspaces"
] | null | 2,315 | 2104.10105 | title_snapshot |
CYO5T-YjWZV | Simple Spectral Graph Convolution | https://openreview.net/forum?id=CYO5T-YjWZV | [
"Hao Zhu",
"Piotr Koniusz"
] | Poster | null | Graph Convolutional Networks (GCNs) are leading methods for learning graph representations. However, without specially designed architectures, the performance of GCNs degrades quickly with increased depth. As the aggregated neighborhood size and neural network depth are two completely orthogonal aspects of graph repres... | [
"Graph Convolutional Network",
"Oversmoothing"
] | null | 2,311 | null | null |
HgLO8yalfwc | Regularized Inverse Reinforcement Learning | https://openreview.net/forum?id=HgLO8yalfwc | [
"Wonseok Jeon",
"Chen-Yang Su",
"Paul Barde",
"Thang Doan",
"Derek Nowrouzezahrai",
"Joelle Pineau"
] | Spotlight | null | Inverse Reinforcement Learning (IRL) aims to facilitate a learner’s ability to imitate expert behavior by acquiring reward functions that explain the expert’s decisions. Regularized IRLapplies strongly convex regularizers to the learner’s policy in order to avoid the expert’s behavior being rationalized by arbitrary co... | [
"inverse reinforcement learning",
"reward learning",
"regularized markov decision processes",
"reinforcement learning"
] | null | 2,310 | 2010.03691 | title_snapshot |
LhY8QdUGSuw | Anatomy of Catastrophic Forgetting: Hidden Representations and Task Semantics | https://openreview.net/forum?id=LhY8QdUGSuw | [
"Vinay Venkatesh Ramasesh",
"Ethan Dyer",
"Maithra Raghu"
] | Poster | null | Catastrophic forgetting is a recurring challenge to developing versatile deep learning models. Despite its ubiquity, there is limited understanding of its connections to neural network (hidden) representations and task semantics. In this paper, we address this important knowledge gap. Through quantitative analysis of n... | [
"Catastrophic forgetting",
"continual learning",
"representation analysis",
"representation learning"
] | null | 2,304 | 2007.07400 | title_snapshot |
o81ZyBCojoA | On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning | https://openreview.net/forum?id=o81ZyBCojoA | [
"Ren Wang",
"Kaidi Xu",
"Sijia Liu",
"Pin-Yu Chen",
"Tsui-Wei Weng",
"Chuang Gan",
"Meng Wang"
] | Poster | null | Model-agnostic meta-learning (MAML) has emerged as one of the most successful meta-learning techniques in few-shot learning. It enables us to learn a $\textit{meta-initialization}$ of model parameters (that we call $\textit{meta-model}$) to rapidly adapt to new tasks using a small amount of labeled training data. Despi... | [] | null | 2,301 | 2102.10454 | title_snapshot |
30EvkP2aQLD | What are the Statistical Limits of Offline RL with Linear Function Approximation? | https://openreview.net/forum?id=30EvkP2aQLD | [
"Ruosong Wang",
"Dean Foster",
"Sham M. Kakade"
] | Spotlight | null | Offline reinforcement learning seeks to utilize offline (observational) data to guide the learning of (causal) sequential decision making strategies. The hope is that offline reinforcement learning coupled with function approximation methods (to deal with the curse of dimensionality) can provide a means to help allevia... | [
"batch reinforcement learning",
"function approximation",
"lower bound",
"representation"
] | null | 2,298 | 2010.11895 | title_snapshot |
42kiJ7n_8xO | The geometry of integration in text classification RNNs | https://openreview.net/forum?id=42kiJ7n_8xO | [
"Kyle Aitken",
"Vinay Venkatesh Ramasesh",
"Ankush Garg",
"Yuan Cao",
"David Sussillo",
"Niru Maheswaranathan"
] | Poster | null | Despite the widespread application of recurrent neural networks (RNNs), a unified understanding of how RNNs solve particular tasks remains elusive. In particular, it is unclear what dynamical patterns arise in trained RNNs, and how those pat-terns depend on the training dataset or task. This work addresses these ques... | [
"Recurrent neural networks",
"dynamical systems",
"interpretability",
"document classification",
"reverse engineering"
] | null | 2,285 | 2010.15114 | title_snapshot |
zrT3HcsWSAt | Behavioral Cloning from Noisy Demonstrations | https://openreview.net/forum?id=zrT3HcsWSAt | [
"Fumihiro Sasaki",
"Ryota Yamashina"
] | Spotlight | null | We consider the problem of learning an optimal expert behavior policy given noisy demonstrations that contain observations from both optimal and non-optimal expert behaviors. Popular imitation learning algorithms, such as generative adversarial imitation learning, assume that (clear) demonstrations are given from optim... | [
"Imitation Learning",
"Inverse Reinforcement Learning",
"Noisy Demonstrations"
] | null | 2,279 | null | null |
2AL06y9cDE- | Towards Robust Neural Networks via Close-loop Control | https://openreview.net/forum?id=2AL06y9cDE- | [
"Zhuotong Chen",
"Qianxiao Li",
"Zheng Zhang"
] | Poster | null | Despite their success in massive engineering applications, deep neural networks are vulnerable to various perturbations due to their black-box nature. Recent study has shown that a deep neural network can misclassify the data even if the input data is perturbed by an imperceptible amount. In this paper, we address the ... | [
"neural network robustness",
"optimal control",
"dynamical system"
] | null | 2,276 | 2102.01862 | title_snapshot |
MBpHUFrcG2x | Projected Latent Markov Chain Monte Carlo: Conditional Sampling of Normalizing Flows | https://openreview.net/forum?id=MBpHUFrcG2x | [
"Chris Cannella",
"Mohammadreza Soltani",
"Vahid Tarokh"
] | Poster | null | We introduce Projected Latent Markov Chain Monte Carlo (PL-MCMC), a technique for sampling from the exact conditional distributions learned by normalizing flows. As a conditional sampling method, PL-MCMC enables Monte Carlo Expectation Maximization (MC-EM) training of normalizing flows from incomplete data. Through exp... | [
"Conditional Sampling",
"Normalizing Flows",
"Markov Chain Monte Carlo",
"Missing Data Inference"
] | null | 2,275 | 2007.06140 | title_snapshot |
8yKEo06dKNo | How Does Mixup Help With Robustness and Generalization? | https://openreview.net/forum?id=8yKEo06dKNo | [
"Linjun Zhang",
"Zhun Deng",
"Kenji Kawaguchi",
"Amirata Ghorbani",
"James Zou"
] | Spotlight | null | Mixup is a popular data augmentation technique based on on convex combinations of pairs of examples and their labels. This simple technique has shown to substantially improve both the model's robustness as well as the generalization of the trained model. However, it is not well-understood why such improvement occurs. ... | [
"Mixup",
"adversarial robustness",
"generalization"
] | null | 2,273 | 2010.04819 | title_snapshot |
fSTD6NFIW_b | Understanding the failure modes of out-of-distribution generalization | https://openreview.net/forum?id=fSTD6NFIW_b | [
"Vaishnavh Nagarajan",
"Anders Andreassen",
"Behnam Neyshabur"
] | Poster | null | Empirical studies suggest that machine learning models often rely on features, such as the background, that may be spuriously correlated with the label only during training time, resulting in poor accuracy during test-time. In this work, we identify the fundamental factors that give rise to this behavior, by explaining... | [
"out-of-distribution generalization",
"spurious correlations",
"empirical risk minimization",
"theoretical study"
] | null | 2,272 | 2010.15775 | title_snapshot |
p8agn6bmTbr | Usable Information and Evolution of Optimal Representations During Training | https://openreview.net/forum?id=p8agn6bmTbr | [
"Michael Kleinman",
"Alessandro Achille",
"Daksh Idnani",
"Jonathan Kao"
] | Poster | null | We introduce a notion of usable information contained in the representation learned by a deep network, and use it to study how optimal representations for the task emerge during training. We show that the implicit regularization coming from training with Stochastic Gradient Descent with a high learning-rate and small b... | [
"Usable Information",
"Representation Learning",
"Learning Dynamics",
"Initialization",
"SGD"
] | null | 2,269 | 2010.02459 | title_snapshot |
R0a0kFI3dJx | Adaptive Extra-Gradient Methods for Min-Max Optimization and Games | https://openreview.net/forum?id=R0a0kFI3dJx | [
"Kimon Antonakopoulos",
"Veronica Belmega",
"Panayotis Mertikopoulos"
] | Poster | null | We present a new family of min-max optimization algorithms that automatically exploit the geometry of the gradient data observed at earlier iterations to perform more informative extra-gradient steps in later ones.
Thanks to this adaptation mechanism, the proposed method automatically detects whether the problem is smo... | [
"min-max optimization",
"games",
"mirror-prox",
"adaptive methods",
"regime agnostic methods"
] | null | 2,265 | 2010.12100 | title_snapshot |
LSFCEb3GYU7 | Emergent Symbols through Binding in External Memory | https://openreview.net/forum?id=LSFCEb3GYU7 | [
"Taylor Whittington Webb",
"Ishan Sinha",
"Jonathan Cohen"
] | Spotlight | null | A key aspect of human intelligence is the ability to infer abstract rules directly from high-dimensional sensory data, and to do so given only a limited amount of training experience. Deep neural network algorithms have proven to be a powerful tool for learning directly from high-dimensional data, but currently lack th... | [
"abstract rules",
"out-of-distribution generalization",
"external memory",
"indirection",
"variable binding"
] | null | 2,264 | 2012.14601 | title_snapshot |
OPyWRrcjVQw | Shapley explainability on the data manifold | https://openreview.net/forum?id=OPyWRrcjVQw | [
"Christopher Frye",
"Damien de Mijolla",
"Tom Begley",
"Laurence Cowton",
"Megan Stanley",
"Ilya Feige"
] | Poster | null | Explainability in AI is crucial for model development, compliance with regulation, and providing operational nuance to predictions. The Shapley framework for explainability attributes a model’s predictions to its input features in a mathematically principled and model-agnostic way. However, general implementations of S... | [] | null | 2,261 | 2006.01272 | title_snapshot |
QFYnKlBJYR | Reinforcement Learning with Random Delays | https://openreview.net/forum?id=QFYnKlBJYR | [
"Yann Bouteiller",
"Simon Ramstedt",
"Giovanni Beltrame",
"Christopher Pal",
"Jonathan Binas"
] | Poster | null | Action and observation delays commonly occur in many Reinforcement Learning applications, such as remote control scenarios. We study the anatomy of randomly delayed environments, and show that partially resampling trajectory fragments in hindsight allows for off-policy multi-step value estimation. We apply this princip... | [
"Reinforcement Learning",
"Deep Reinforcement Learning"
] | null | 2,259 | 2010.02966 | title_snapshot |
JBAa9we1AL | Individually Fair Gradient Boosting | https://openreview.net/forum?id=JBAa9we1AL | [
"Alexander Vargo",
"Fan Zhang",
"Mikhail Yurochkin",
"Yuekai Sun"
] | Spotlight | null | We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in applications where algorithmic fairness is a concern. At a high level, our approach is a functional gradient descent on a (distributionally) robu... | [
"Algorithmic fairness",
"boosting",
"non-smooth models"
] | null | 2,257 | 2103.16785 | title_snapshot |
NcFEZOi-rLa | Shape or Texture: Understanding Discriminative Features in CNNs | https://openreview.net/forum?id=NcFEZOi-rLa | [
"Md Amirul Islam",
"Matthew Kowal",
"Patrick Esser",
"Sen Jia",
"Björn Ommer",
"Konstantinos G. Derpanis",
"Neil Bruce"
] | Poster | null | Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a 'texture bias': given an image with both texture and shape cues (e.g., a stylized image), a CNN is biased towards predicting... | [
"Shape",
"Texture",
"Shape Bias",
"Texture Bias",
"Shape Encoding",
"Mutual Information"
] | null | 2,256 | 2101.11604 | title_snapshot |
Iw4ZGwenbXf | NOVAS: Non-convex Optimization via Adaptive Stochastic Search for End-to-end Learning and Control | https://openreview.net/forum?id=Iw4ZGwenbXf | [
"Ioannis Exarchos",
"Marcus Aloysius Pereira",
"Ziyi Wang",
"Evangelos Theodorou"
] | Poster | null | In this work we propose the use of adaptive stochastic search as a building block for general, non-convex optimization operations within deep neural network architectures. Specifically, for an objective function located at some layer in the network and parameterized by some network parameters, we employ adaptive stocha... | [
"deep neural networks",
"nested optimization",
"stochastic control",
"deep FBSDEs"
] | null | 2,254 | 2006.11992 | title_snapshot |
DktZb97_Fx | SenSeI: Sensitive Set Invariance for Enforcing Individual Fairness | https://openreview.net/forum?id=DktZb97_Fx | [
"Mikhail Yurochkin",
"Yuekai Sun"
] | Oral | null | In this paper, we cast fair machine learning as invariant machine learning. We first formulate a version of individual fairness that enforces invariance on certain sensitive sets. We then design a transport-based regularizer that enforces this version of individual fairness and develop an algorithm to minimize the regu... | [
"Algorithmic fairness",
"invariance"
] | null | 2,251 | 2006.14168 | title_snapshot |
rC8sJ4i6kaH | Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data | https://openreview.net/forum?id=rC8sJ4i6kaH | [
"Colin Wei",
"Kendrick Shen",
"Yining Chen",
"Tengyu Ma"
] | Oral | null | Self-training algorithms, which train a model to fit pseudolabels predicted by another previously-learned model, have been very successful for learning with unlabeled data using neural networks. However, the current theoretical understanding of self-training only applies to linear models. This work provides a unified t... | [
"deep learning theory",
"domain adaptation theory",
"unsupervised learning theory",
"semi-supervised learning theory"
] | null | 2,250 | 2010.03622 | title_snapshot |
Ovp8dvB8IBH | Negative Data Augmentation | https://openreview.net/forum?id=Ovp8dvB8IBH | [
"Abhishek Sinha",
"Kumar Ayush",
"Jiaming Song",
"Burak Uzkent",
"Hongxia Jin",
"Stefano Ermon"
] | Poster | null | Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations, we explore negative data augmentation strategies (NDA) that intentionally create out-of-distribution samples. We show that such negative out... | [
"generative models",
"self-supervised learning",
"data augmentation",
"anomaly detection"
] | null | 2,248 | 2102.05113 | title_snapshot |
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