ICLR 2022

1,094 papers

cosFormer: Rethinking Softmax In Attention

Zhen Qin, Weixuan Sun, Hui Deng, DONGXU LI, Yunshen Wei

Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes

Yunjiang Jiang, Han Zhang, Yiming Qiu, Yun Xiao, Bo Long

Exploiting Class Activation Value for Partial-Label Learning

Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu

Measuring CLEVRness: Black-box Testing of Visual Reasoning Models

Spyridon Mouselinos, Henryk Michalewski, Mateusz Malinowski

Learning Graphon Mean Field Games and Approximate Nash Equilibria

Kai Cui, Heinz Koeppl

On Robust Prefix-Tuning for Text Classification

Zonghan Yang, Yang Liu

Explaining Point Processes by Learning Interpretable Temporal Logic Rules

Shuang Li, Mingquan Feng, Lu Wang, Abdelmajid Essofi, Yufeng Cao

DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning

Jinxin Liu, Zhang Hongyin, Donglin Wang

TAPEX: Table Pre-training via Learning a Neural SQL Executor

Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin

On-Policy Model Errors in Reinforcement Learning

Lukas Froehlich, Maksym Lefarov, Melanie Zeilinger, Felix Berkenkamp

Imitation Learning by Reinforcement Learning

Kamil Ciosek

Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation

Yang Zhao, Hao Zhang

Partial Wasserstein Adversarial Network for Non-rigid Point Set Registration

Ziming Wang, Nan Xue, Ling Lei, Gui-Song Xia

Visual hyperacuity with moving sensor and recurrent neural computations

Alexander Rivkind, Or Ram, Eldad Assa, Michael Kreiserman, Ehud Ahissar

Delaunay Component Analysis for Evaluation of Data Representations

Petra Poklukar, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny, Danica Kragic Jensfelt

Neural Program Synthesis with Query

Di Huang, Rui Zhang, Xing Hu, xishan zhang, Pengwei Jin

Reliable Adversarial Distillation with Unreliable Teachers

Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng ZHANG, Tongliang Liu

Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting

Shikuang Deng, Yuhang Li, Shanghang Zhang, Shi Gu

SUMNAS: Supernet with Unbiased Meta-Features for Neural Architecture Search

Hyeonmin Ha, Ji-Hoon Kim, Semin Park, Byung-gon Chun

High Probability Generalization Bounds with Fast Rates for Minimax Problems

Shaojie Li, Yong Liu

VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning

Adrien Bardes, Jean Ponce, Yann LeCun

Gradient Information Matters in Policy Optimization by Back-propagating through Model

Chongchong Li, Yue Wang, Wei Chen, Yuting Liu, Zhi-ming Ma

Policy Gradients Incorporating the Future

David Venuto, Elaine Lau, Doina Precup, Ofir Nachum

Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game

Haobo Fu, Weiming Liu, Shuang Wu, Yijia Wang, Tao Yang

Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms

Simin Hong, Anthony Cohn, David Crossland Hogg

Subspace Regularizers for Few-Shot Class Incremental Learning

Afra Feyza Akyürek, Ekin Akyürek, Derry Wijaya, Jacob Andreas

How Well Does Self-Supervised Pre-Training Perform with Streaming Data?

Dapeng Hu, Shipeng Yan, Qizhengqiu Lu, Lanqing Hong, Hailin Hu

Peek-a-Boo: What (More) is Disguised in a Randomly Weighted Neural Network, and How to Find It Efficiently

Xiaohan Chen, Jason Zhang, Zhangyang Wang

Noisy Feature Mixup

Soon Hoe Lim, N. Benjamin Erichson, Francisco Utrera, Winnie Xu, Michael W. Mahoney

Evidential Turing Processes

Melih Kandemir, Abdullah Akgül, Manuel Haussmann, Gozde Unal

Dive Deeper Into Integral Pose Regression

Kerui Gu, Linlin Yang, Angela Yao

Constraining Linear-chain CRFs to Regular Languages

Sean Papay, Roman Klinger, Sebastian Padó

Understanding approximate and unrolled dictionary learning for pattern recovery

Benoît Malézieux, Thomas Moreau, Matthieu Kowalski

Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information

Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtarik, Michael W. Mahoney

Constrained Physical-Statistics Models for Dynamical System Identification and Prediction

Jérémie Donà, Marie Déchelle, Patrick Gallinari, Marina Levy

Distributionally Robust Models with Parametric Likelihood Ratios

Paul Michel, Tatsunori Hashimoto, Graham Neubig

ADAVI: Automatic Dual Amortized Variational Inference Applied To Pyramidal Bayesian Models

Louis Rouillard, Demian Wassermann

Recursive Disentanglement Network

Yixuan Chen, Yubin Shi, Dongsheng Li, Yujiang Wang, MINGZHI DONG

On the Limitations of Multimodal VAEs

Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E Vogt

The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization

Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber

Training Structured Neural Networks Through Manifold Identification and Variance Reduction

Zih-Syuan Huang, Ching-pei Lee

Ancestral protein sequence reconstruction using a tree-structured Ornstein-Uhlenbeck variational autoencoder

Lys Sanz Moreta, Ola Rønning, Ahmad Salim Al-Sibahi, Jotun Hein, Douglas Theobald

Efficient Active Search for Combinatorial Optimization Problems

André Hottung, Yeong-Dae Kwon, Kevin Tierney

Information Prioritization through Empowerment in Visual Model-based RL

Homanga Bharadhwaj, Mohammad Babaeizadeh, Dumitru Erhan, Sergey Levine

FILIP: Fine-grained Interactive Language-Image Pre-Training

Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu

Effective Model Sparsification by Scheduled Grow-and-Prune Methods

Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan

Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities

Jianda Chen, Sinno Pan

Lipschitz-constrained Unsupervised Skill Discovery

Seohong Park, Jongwook Choi, Jaekyeom Kim, Honglak Lee, Gunhee Kim

Efficient Sharpness-aware Minimization for Improved Training of Neural Networks

JIAWEI DU, Hanshu Yan, Jiashi Feng, Joey Tianyi Zhou, Liangli Zhen

The Geometry of Memoryless Stochastic Policy Optimization in Infinite-Horizon POMDPs

Johannes Müller, Guido Montúfar

A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications

Fuchao Wei, Chenglong Bao, Yang Liu

Coherence-based Label Propagation over Time Series for Accelerated Active Learning

Yooju Shin, Susik Yoon, Sundong Kim, Hwanjun Song, Jae-Gil Lee

Plant 'n' Seek: Can You Find the Winning Ticket?

Jonas Fischer, Rebekka Burkholz

CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture

Ruofan Liang, Hongyi Sun, Nandita Vijaykumar

CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Stiffness-aware neural network for learning Hamiltonian systems

Senwei Liang, Zhongzhan Huang, Hong Zhang

DriPP: Driven Point Processes to Model Stimuli Induced Patterns in M/EEG Signals

Cédric Allain, Alexandre Gramfort, Thomas Moreau

A Generalized Weighted Optimization Method for Computational Learning and Inversion

Kui Ren, Yunan Yang, Björn Engquist

πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization

Carl Hvarfner, Danny Stoll, Artur Souza, Marius Lindauer, Frank Hutter

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

Denis Yarats, Rob Fergus, Alessandro Lazaric, Lerrel Pinto

Implicit Bias of Adversarial Training for Deep Neural Networks

Bochen Lv, Zhanxing Zhu

The Uncanny Similarity of Recurrence and Depth

Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein

Sample Efficient Stochastic Policy Extragradient Algorithm for Zero-Sum Markov Game

Ziyi Chen, Shaocong Ma, Yi Zhou

BDDM: Bilateral Denoising Diffusion Models for Fast and High-Quality Speech Synthesis

Max W. Y. Lam, Jun Wang, Dan Su, Dong Yu

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models

Yang Song, Liyue Shen, Lei Xing, Stefano Ermon

Finding an Unsupervised Image Segmenter in each of your Deep Generative Models

Luke Melas-Kyriazi, Christian Rupprecht, Iro Laina, Andrea Vedaldi

Controlling the Complexity and Lipschitz Constant improves Polynomial Nets

Zhenyu Zhu, Fabian Latorre, Grigorios Chrysos, Volkan Cevher

Sparse Attention with Learning to Hash

Zhiqing Sun, Yiming Yang, Shinjae Yoo

Bridging Recommendation and Marketing via Recurrent Intensity Modeling

Yifei Ma, Ge Liu, Anoop Deoras

CodeTrek: Flexible Modeling of Code using an Extensible Relational Representation

Pardis Pashakhanloo, Aaditya Naik, Yuepeng Wang, Hanjun Dai, Petros Maniatis

Semi-relaxed Gromov-Wasserstein divergence and applications on graphs

Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty

Label Leakage and Protection in Two-party Split Learning

Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang

CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals

Cédric ROMMEL, Thomas Moreau, Joseph Paillard, Alexandre Gramfort

A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features

Zhenmei Shi, Junyi Wei, Yingyu Liang

Neural Deep Equilibrium Solvers

Shaojie Bai, Vladlen Koltun, J Zico Kolter

Case-based reasoning for better generalization in textual reinforcement learning

Mattia Atzeni, Shehzaad Zuzar Dhuliawala, Keerthiram Murugesan, Mrinmaya Sachan

GRAND++: Graph Neural Diffusion with A Source Term

Matthew Thorpe, Tan Minh Nguyen, Hedi Xia, Thomas Strohmer, Andrea Bertozzi

Autonomous Reinforcement Learning: Formalism and Benchmarking

Archit Sharma, Kelvin Xu, Nikhil Sardana, Abhishek Gupta, Karol Hausman

Fast topological clustering with Wasserstein distance

Tananun Songdechakraiwut, Bryan M Krause, Matthew I Banks, Kirill V Nourski, Barry D Van Veen

Learning Curves for Gaussian Process Regression with Power-Law Priors and Targets

Hui Jin, Pradeep Kr. Banerjee, Guido Montúfar

Frequency-aware SGD for Efficient Embedding Learning with Provable Benefits

Yan Li, Dhruv Choudhary, Xiaohan Wei, Baichuan Yuan, Bhargav Bhushanam

What Do We Mean by Generalization in Federated Learning?

Honglin Yuan, Warren Richard Morningstar, Lin Ning, Karan Singhal

FedChain: Chained Algorithms for Near-optimal Communication Cost in Federated Learning

Charlie Hou, Kiran Koshy Thekumparampil, Giulia Fanti, Sewoong Oh

Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System

Elahe Arani, Fahad Sarfraz, Bahram Zonooz

Eliminating Sharp Minima from SGD with Truncated Heavy-tailed Noise

Xingyu Wang, Sewoong Oh, Chang-Han Rhee

Differentiable Scaffolding Tree for Molecule Optimization

Tianfan Fu, Wenhao Gao, Cao Xiao, Jacob Yasonik, Connor W. Coley

An Explanation of In-context Learning as Implicit Bayesian Inference

Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma

Learning Audio-Visual Speech Representation by Masked Multimodal Cluster Prediction

Bowen Shi, Wei-Ning Hsu, Kushal Lakhotia, Abdelrahman Mohamed

Hierarchical Variational Memory for Few-shot Learning Across Domains

Yingjun Du, Xiantong Zhen, Ling Shao, Cees G. M. Snoek

Taming Sparsely Activated Transformer with Stochastic Experts

Simiao Zuo, Xiaodong Liu, Jian Jiao, Young Jin Kim, Hany Hassan

PER-ETD: A Polynomially Efficient Emphatic Temporal Difference Learning Method

Ziwei Guan, Tengyu Xu, Yingbin Liang

Pre-training Molecular Graph Representation with 3D Geometry

Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo

On the Existence of Universal Lottery Tickets

Rebekka Burkholz, Nilanjana Laha, Rajarshi Mukherjee, Alkis Gotovos

DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools

Xingyu Lin, Zhiao Huang, Yunzhu Li, Joshua B. Tenenbaum, David Held

On Incorporating Inductive Biases into VAEs

Ning Miao, Emile Mathieu, Siddharth N, Yee Whye Teh, Tom Rainforth

An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch

Youzhi Luo, Shuiwang Ji

Eigencurve: Optimal Learning Rate Schedule for SGD on Quadratic Objectives with Skewed Hessian Spectrums

Rui Pan, Haishan Ye, Tong Zhang

Fast Model Editing at Scale

Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D Manning

Transformer Embeddings of Irregularly Spaced Events and Their Participants

Hongyuan Mei, Chenghao Yang, Jason Eisner

Federated Learning from Only Unlabeled Data with Class-conditional-sharing Clients

Nan Lu, Zhao Wang, Xiaoxiao Li, Gang Niu, Qi Dou