NeurIPS 2020

1,898 papers

Limits to Depth Efficiencies of Self-Attention

Yoav Levine, Noam Wies, Or Sharir, Hofit Bata, Amnon Shashua

Sampling-Decomposable Generative Adversarial Recommender

Binbin Jin, Defu Lian, Zheng Liu, Qi Liu, Jianhui Ma

Passport-aware Normalization for Deep Model Protection

Jie Zhang, Dongdong Chen, Jing Liao, Weiming Zhang, Gang Hua

COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning

Simon Ging, Mohammadreza Zolfaghari, Hamed Pirsiavash, Thomas Brox

Distributed Distillation for On-Device Learning

Ilai Bistritz, Ariana Mann, Nicholas Bambos

Manifold GPLVMs for discovering non-Euclidean latent structure in neural data

Kristopher Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin

Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks

Yuhang Song, Thomas Lukasiewicz, Zhenghua Xu, Rafal Bogacz

An Analysis of SVD for Deep Rotation Estimation

Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa

Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping

Shashanka Ubaru, Sanjeeb Dash, Arya Mazumdar, Oktay Gunluk

Multi-agent Trajectory Prediction with Fuzzy Query Attention

Nitin Kamra, Hao Zhu, Dweep Kumarbhai Trivedi, Ming Zhang, Yan Liu

Wasserstein Distances for Stereo Disparity Estimation

Divyansh Garg, Yan Wang, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger

Faithful Embeddings for Knowledge Base Queries

Haitian Sun, Andrew Arnold, Tania Bedrax Weiss, Fernando Pereira, William W. Cohen

Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning

Fei Feng, Ruosong Wang, Wotao Yin, Simon S Du, Lin Yang

Variational Interaction Information Maximization for Cross-domain Disentanglement

HyeongJoo Hwang, Geon-hyeong Kim, Seunghoon Hong, Kee-eung Kim

MeshSDF: Differentiable Iso-Surface Extraction

Edoardo Remelli, Artem Lukoianov, Stephan Richter, Benoit Guillard, Timur Bagautdinov

Matrix Inference and Estimation in Multi-Layer Models

Parthe Pandit, Mojtaba Sahraee Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher

PLANS: Neuro-Symbolic Program Learning from Videos

Raphaël Dang-Nhu

Multiparameter Persistence Image for Topological Machine Learning

Mathieu Carrière, Andrew Blumberg

De-Anonymizing Text by Fingerprinting Language Generation

Zhen Sun, Roei Schuster, Vitaly Shmatikov

Faster DBSCAN via subsampled similarity queries

Heinrich Jiang, Jennifer Jang, Jakub Lacki

Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes

Salman Habib, Allison Beemer, Joerg Kliewer

Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret

Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang, Qiaomin Xie

Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks

Lemeng Wu, Bo Liu, Peter Stone, Qiang Liu

Scalable Belief Propagation via Relaxed Scheduling

Vitalii Aksenov, Dan Alistarh, Janne H. Korhonen

Synthesizing Tasks for Block-based Programming

Umair Ahmed, Maria Christakis, Aleksandr Efremov, Nigel Fernandez, Ahana Ghosh

Learning to Adapt to Evolving Domains

Hong Liu, Mingsheng Long, Jianmin Wang, Yu Wang

Agnostic Q-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity

Simon S Du, Jason Lee, Gaurav Mahajan, Ruosong Wang

Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games

Arun Suggala, Praneeth Netrapalli

Model Class Reliance for Random Forests

Gavin Smith, Roberto Mansilla, James Goulding

Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning

Nathan Kallus, Angela Zhou

Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs

Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory Slabaugh

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization

Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob Gardner

Learning from Label Proportions: A Mutual Contamination Framework

Clayton Scott, Jianxin Zhang

Big Self-Supervised Models are Strong Semi-Supervised Learners

Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton

Measuring Systematic Generalization in Neural Proof Generation with Transformers

Nicolas Gontier, Koustuv Sinha, Siva Reddy, Chris Pal

A Dynamical Central Limit Theorem for Shallow Neural Networks

Zhengdao Chen, Grant Rotskoff, Joan Bruna, Eric Vanden-Eijnden

Auditing Differentially Private Machine Learning: How Private is Private SGD?

Matthew Jagielski, Jonathan Ullman, Alina Oprea

Focus of Attention Improves Information Transfer in Visual Features

Matteo Tiezzi, Stefano Melacci, Alessandro Betti, Marco Maggini, Marco Gori

Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy

Edward Moroshko, Blake E Woodworth, Suriya Gunasekar, Jason Lee, Nati Srebro

MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models

Sourav Biswas, Jerry Liu, Kelvin Wong, Shenlong Wang, Raquel Urtasun

Hierarchical Neural Architecture Search for Deep Stereo Matching

Xuelian Cheng, Yiran Zhong, Mehrtash Harandi, Yuchao Dai, Xiaojun Chang

Texture Interpolation for Probing Visual Perception

Jonathan Vacher, Aida Davila, Adam Kohn, Ruben Coen-cagli

Towards Understanding Hierarchical Learning: Benefits of Neural Representations

Minshuo Chen, Yu Bai, Jason Lee, Tuo Zhao, Huan Wang

Open Graph Benchmark: Datasets for Machine Learning on Graphs

Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren

Gradient Boosted Normalizing Flows

Robert Giaquinto, Arindam Banerjee

Graph Random Neural Networks for Semi-Supervised Learning on Graphs

Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan

Set2Graph: Learning Graphs From Sets

Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross

Learning Individually Inferred Communication for Multi-Agent Cooperation

Ziluo Ding, Tiejun Huang, Zongqing Lu

On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems

Kaiqing Zhang, Bin Hu, Tamer Basar

Model Fusion via Optimal Transport

Sidak Pal Singh, Martin Jaggi

High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization

Qing Feng, Ben Letham, Hongzi Mao, Eytan Bakshy

Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation

Bowen Li, Xiaojuan Qi, Philip Torr, Thomas Lukasiewicz

SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology

Mark Veillette, Siddharth Samsi, Chris Mattioli

Learning Latent Space Energy-Based Prior Model

Bo Pang, Tian Han, Erik Nijkamp, Song-chun Zhu, Ying Nian Wu

CrossTransformers: spatially-aware few-shot transfer

Carl Doersch, Ankush Gupta, Andrew Zisserman

Attribute Prototype Network for Zero-Shot Learning

Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata

Minibatch Stochastic Approximate Proximal Point Methods

Hilal Asi, Karan Chadha, Gary Cheng, John C. Duchi

Denoised Smoothing: A Provable Defense for Pretrained Classifiers

Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, J. Zico Kolter

Beyond Lazy Training for Over-parameterized Tensor Decomposition

Xiang Wang, Chenwei Wu, Jason Lee, Tengyu Ma, Rong Ge

Goal-directed Generation of Discrete Structures with Conditional Generative Models

Amina Mollaysa, Brooks Paige, Alexandros Kalousis

Contextual Games: Multi-Agent Learning with Side Information

Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause, Maryam Kamgarpour

Robust Persistence Diagrams using Reproducing Kernels

Siddharth Vishwanath, Kenji Fukumizu, Satoshi Kuriki, Bharath K. Sriperumbudur

Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate

Akifumi Okuno, Hidetoshi Shimodaira

One-sample Guided Object Representation Disassembling

Zunlei Feng, Yongming He, Xinchao Wang, Xin Gao, Jie Lei

Differentiable Causal Discovery from Interventional Data

Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin

Learning Semantic-aware Normalization for Generative Adversarial Networks

Heliang Zheng, Jianlong Fu, Yanhong Zeng, Jiebo Luo, Zheng-Jun Zha

Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion

Qianqian Ma, Alex Olshevsky

Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings

Christopher Morris, Gaurav Rattan, Petra Mutzel

MOReL: Model-Based Offline Reinforcement Learning

Rahul Kidambi, Aravind Rajeswaran, Praneeth Netrapalli, Thorsten Joachims

Hard Negative Mixing for Contrastive Learning

Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, Diane Larlus

Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning

Zhongzheng Ren, Raymond Yeh, Alexander Schwing

A General Method for Robust Learning from Batches

Ayush Jain, Alon Orlitsky

Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis

Ye Yuan, Kris Kitani

Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects

Zijun Gao, Yanjun Han

First-Order Methods for Large-Scale Market Equilibrium Computation

Yuan Gao, Christian Kroer

Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians

Juhan Bae, Roger B Grosse

Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation

Ping Hu, Stan Sclaroff, Kate Saenko

AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection

Hao Zhu, Chaoyou Fu, Qianyi Wu, Wayne Wu, Chen Qian

Strongly Incremental Constituency Parsing with Graph Neural Networks

Kaiyu Yang, Jia Deng

The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification

Christian Tjandraatmadja, Ross Anderson, Joey Huchette, Will Ma, KRUNAL KISHOR PATEL

Fast Transformers with Clustered Attention

Apoorv Vyas, Angelos Katharopoulos, Francois Fleuret

Curriculum By Smoothing

Samarth Sinha, Animesh Garg, Hugo Larochelle

Neural Unsigned Distance Fields for Implicit Function Learning

Julian Chibane, Mohamad Aymen mir, Gerard Pons-Moll

H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks

Thomas Limbacher, Robert A. Legenstein

Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms

Xiangyi Chen, Tiancong Chen, Haoran Sun, Steven Z. Wu, Mingyi Hong

Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion

Zhanqiu Zhang, Jianyu Cai, Jie Wang

High-contrast “gaudy” images improve the training of deep neural network models of visual cortex

Benjamin Cowley, Jonathan W Pillow

Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning

Tianren Zhang, Shangqi Guo, Tian Tan, Xiaolin Hu, Feng Chen

Quantile Propagation for Wasserstein-Approximate Gaussian Processes

Rui Zhang, Christian Walder, Edwin V. Bonilla, Marian-Andrei Rizoiu, Lexing Xie

Robust Federated Learning: The Case of Affine Distribution Shifts

Amirhossein Reisizadeh, Farzan Farnia, Ramtin Pedarsani, Ali Jadbabaie

Reconsidering Generative Objectives For Counterfactual Reasoning

Danni Lu, Chenyang Tao, Junya Chen, Fan Li, Feng Guo

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization

Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham

Convergence and Stability of Graph Convolutional Networks on Large Random Graphs

Nicolas Keriven, Alberto Bietti, Samuel Vaiter

Active Structure Learning of Causal DAGs via Directed Clique Trees

Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu

User-Dependent Neural Sequence Models for Continuous-Time Event Data

Alex Boyd, Robert Bamler, Stephan Mandt, Padhraic Smyth

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Chen Liu, Mathieu Salzmann, Tao LIN, Ryota Tomioka, Sabine Süsstrunk

Energy-based Out-of-distribution Detection

Weitang Liu, Xiaoyun Wang, John Owens, Yixuan Li

A novel variational form of the Schatten-p quasi-norm

Paris Giampouras, René Vidal, Athanasios Rontogiannis, Benjamin Haeffele

Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters

Sulin Liu, Xingyuan Sun, Peter J. Ramadge, Ryan P. Adams

Geometric Dataset Distances via Optimal Transport

David Alvarez-Melis, Nicolo Fusi