ICLR 2022
1094 papers
8-Bit Optimizers via Block-Wise Quantization
Tim Dettmers, Mike Lewis, Sam Shleifer, Luke Zettlemoyer A Deep Variational Approach to Clustering Survival Data
Laura Manduchi, Ričards Marcinkevičs, Michela C. Massi, Thomas Weikert, Alexander Sauter, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Bram Stieltjes, Julia E Vogt A Fine-Grained Analysis on Distribution Shift
Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre-Alvise Rebuffi, Ira Ktena, Krishnamurthy Dj Dvijotham, Ali Taylan Cemgil A Fine-Tuning Approach to Belief State Modeling
Samuel Sokota, Hengyuan Hu, David J Wu, J Zico Kolter, Jakob Nicolaus Foerster, Noam Brown A Loss Curvature Perspective on Training Instabilities of Deep Learning Models
Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George Edward Dahl, Zachary Nado, Orhan Firat A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning
Yunchang Yang, Tianhao Wu, Han Zhong, Evrard Garcelon, Matteo Pirotta, Alessandro Lazaric, Liwei Wang, Simon Shaolei Du A Theory of Tournament Representations
Arun Rajkumar, Vishnu Veerathu, Abdul Bakey Mir A Zest of LIME: Towards Architecture-Independent Model Distances
Hengrui Jia, Hongyu Chen, Jonas Guan, Ali Shahin Shamsabadi, Nicolas Papernot Accelerated Policy Learning with Parallel Differentiable Simulation
Jie Xu, Viktor Makoviychuk, Yashraj Narang, Fabio Ramos, Wojciech Matusik, Animesh Garg, Miles Macklin Actor-Critic Policy Optimization in a Large-Scale Imperfect-Information Game
Haobo Fu, Weiming Liu, Shuang Wu, Yijia Wang, Tao Yang, Kai Li, Junliang Xing, Bin Li, Bo Ma, Qiang Fu, Yang Wei Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space
Yaohua Wang, Yaobin Zhang, Fangyi Zhang, Senzhang Wang, Ming Lin, YuQi Zhang, Xiuyu Sun AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
David Berthelot, Rebecca Roelofs, Kihyuk Sohn, Nicholas Carlini, Alexey Kurakin Adversarial Retriever-Ranker for Dense Text Retrieval
Hang Zhang, Yeyun Gong, Yelong Shen, Jiancheng Lv, Nan Duan, Weizhu Chen Adversarial Robustness Through the Lens of Causality
Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang Adversarial Support Alignment
Shangyuan Tong, Timur Garipov, Yang Zhang, Shiyu Chang, Tommi S. Jaakkola Adversarial Unlearning of Backdoors via Implicit Hypergradient
Yi Zeng, Si Chen, Won Park, Zhuoqing Mao, Ming Jin, Ruoxi Jia Adversarially Robust Conformal Prediction
Asaf Gendler, Tsui-Wei Weng, Luca Daniel, Yaniv Romano AlphaZero-Based Proof Cost Network to Aid Game Solving
Ti-Rong Wu, Chung-Chin Shih, Ting Han Wei, Meng-Yu Tsai, Wei-Yuan Hsu, I-Chen Wu An Experimental Design Perspective on Model-Based Reinforcement Learning
Viraj Mehta, Biswajit Paria, Jeff Schneider, Stefano Ermon, Willie Neiswanger An Information Fusion Approach to Learning with Instance-Dependent Label Noise
Zhimeng Jiang, Kaixiong Zhou, Zirui Liu, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu An Operator Theoretic View on Pruning Deep Neural Networks
William T Redman, Maria Fonoberova, Ryan Mohr, Yannis Kevrekidis, Igor Mezic An Unconstrained Layer-Peeled Perspective on Neural Collapse
Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, Weijie J Su Anti-Concentrated Confidence Bonuses for Scalable Exploration
Jordan T. Ash, Cyril Zhang, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade Anytime Dense Prediction with Confidence Adaptivity
Zhuang Liu, Zhiqiu Xu, Hung-Ju Wang, Trevor Darrell, Evan Shelhamer Assessing Generalization of SGD via Disagreement
Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, J Zico Kolter Attention-Based Interpretability with Concept Transformers
Mattia Rigotti, Christoph Miksovic, Ioana Giurgiu, Thomas Gschwind, Paolo Scotton Augmented Sliced Wasserstein Distances
Xiongjie Chen, Yongxin Yang, Yunpeng Li Auto-Scaling Vision Transformers Without Training
Wuyang Chen, Wei Huang, Xianzhi Du, Xiaodan Song, Zhangyang Wang, Denny Zhou Auto-Transfer: Learning to Route Transferable Representations
Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar Automated Self-Supervised Learning for Graphs
Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, Jiliang Tang Autonomous Reinforcement Learning: Formalism and Benchmarking
Archit Sharma, Kelvin Xu, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn Autoregressive Diffusion Models
Emiel Hoogeboom, Alexey A. Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, Tim Salimans Backdoor Defense via Decoupling the Training Process
Kunzhe Huang, Yiming Li, Baoyuan Wu, Zhan Qin, Kui Ren BadPre: Task-Agnostic Backdoor Attacks to Pre-Trained NLP Foundation Models
Kangjie Chen, Yuxian Meng, Xiaofei Sun, Shangwei Guo, Tianwei Zhang, Jiwei Li, Chun Fan Bag of Instances Aggregation Boosts Self-Supervised Distillation
Haohang Xu, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, Xinggang Wang, Wenrui Dai, Hongkai Xiong, Qi Tian BAM: Bayes with Adaptive Memory
Josue Nassar, Jennifer Rogers Brennan, Ben Evans, Kendall Lowrey Bayesian Framework for Gradient Leakage
Mislav Balunovic, Dimitar Iliev Dimitrov, Robin Staab, Martin Vechev Bayesian Neural Network Priors Revisited
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Ratsch, Richard E Turner, Mark van der Wilk, Laurence Aitchison Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-Box Domains
Qilong Zhang, Xiaodan Li, YueFeng Chen, Jingkuan Song, Lianli Gao, Yuan He, Hui Xue' BiBERT: Accurate Fully Binarized BERT
Haotong Qin, Yifu Ding, Mingyuan Zhang, Qinghua Yan, Aishan Liu, Qingqing Dang, Ziwei Liu, Xianglong Liu Boosted Curriculum Reinforcement Learning
Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen Boosting Randomized Smoothing with Variance Reduced Classifiers
Miklós Z. Horváth, Mark Niklas Mueller, Marc Fischer, Martin Vechev Bootstrapped Meta-Learning
Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy, Hado van Hasselt, David Silver, Satinder Singh Bregman Gradient Policy Optimization
Feihu Huang, Shangqian Gao, Heng Huang C-Planning: An Automatic Curriculum for Learning Goal-Reaching Tasks
Tianjun Zhang, Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine, Joseph E. Gonzalez Capturing Structural Locality in Non-Parametric Language Models
Frank F. Xu, Junxian He, Graham Neubig, Vincent Josua Hellendoorn Case-Based Reasoning for Better Generalization in Textual Reinforcement Learning
Mattia Atzeni, Shehzaad Zuzar Dhuliawala, Keerthiram Murugesan, Mrinmaya Sachan Charformer: Fast Character Transformers via Gradient-Based Subword Tokenization
Yi Tay, Vinh Q. Tran, Sebastian Ruder, Jai Gupta, Hyung Won Chung, Dara Bahri, Zhen Qin, Simon Baumgartner, Cong Yu, Donald Metzler Chemical-Reaction-Aware Molecule Representation Learning
Hongwei Wang, Weijiang Li, Xiaomeng Jin, Kyunghyun Cho, Heng Ji, Jiawei Han, Martin D. Burke Chunked Autoregressive GAN for Conditional Waveform Synthesis
Max Morrison, Rithesh Kumar, Kundan Kumar, Prem Seetharaman, Aaron Courville, Yoshua Bengio Churn Reduction via Distillation
Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh CKConv: Continuous Kernel Convolution for Sequential Data
David W. Romero, Anna Kuzina, Erik J Bekkers, Jakub Mikolaj Tomczak, Mark Hoogendoorn ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Victor Schmidt, Alexandra Luccioni, Mélisande Teng, Tianyu Zhang, Alexia Reynaud, Sunand Raghupathi, Gautier Cosne, Adrien Juraver, Vahe Vardanyan, Alex Hernández-García, Yoshua Bengio CoBERL: Contrastive BERT for Reinforcement Learning
Andrea Banino, Adria Puigdomenech Badia, Jacob C Walker, Tim Scholtes, Jovana Mitrovic, Charles Blundell CodeTrek: Flexible Modeling of Code Using an Extensible Relational Representation
Pardis Pashakhanloo, Aaditya Naik, Yuepeng Wang, Hanjun Dai, Petros Maniatis, Mayur Naik Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods
Wenqing Zheng, Edward W Huang, Nikhil Rao, Sumeet Katariya, Zhangyang Wang, Karthik Subbian Comparing Distributions by Measuring Differences That Affect Decision Making
Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, Stefano Ermon ComPhy: Compositional Physical Reasoning of Objects and Events from Videos
Zhenfang Chen, Kexin Yi, Yunzhu Li, Mingyu Ding, Antonio Torralba, Joshua B. Tenenbaum, Chuang Gan Compositional Attention: Disentangling Search and Retrieval
Sarthak Mittal, Sharath Chandra Raparthy, Irina Rish, Yoshua Bengio, Guillaume Lajoie CoMPS: Continual Meta Policy Search
Glen Berseth, Zhiwei Zhang, Grace Zhang, Chelsea Finn, Sergey Levine Concurrent Adversarial Learning for Large-Batch Training
Yong Liu, Xiangning Chen, Minhao Cheng, Cho-Jui Hsieh, Yang You Conditional Contrastive Learning with Kernel
Yao-Hung Hubert Tsai, Tianqin Li, Martin Q. Ma, Han Zhao, Kun Zhang, Louis-Philippe Morency, Ruslan Salakhutdinov Conditional Object-Centric Learning from Video
Thomas Kipf, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Austin Stone, Sara Sabour, Georg Heigold, Rico Jonschkowski, Alexey Dosovitskiy, Klaus Greff Conditioning Sequence-to-Sequence Networks with Learned Activations
Alberto Gil Couto Pimentel Ramos, Abhinav Mehrotra, Nicholas Donald Lane, Sourav Bhattacharya Connectome-Constrained Latent Variable Model of Whole-Brain Neural Activity
Lu Mi, Richard Xu, Sridhama Prakhya, Albert Lin, Nir Shavit, Aravinthan Samuel, Srinivas C Turaga Constrained Policy Optimization via Bayesian World Models
Yarden As, Ilnura Usmanova, Sebastian Curi, Andreas Krause Context-Aware Sparse Deep Coordination Graphs
Tonghan Wang, Liang Zeng, Weijun Dong, Qianlan Yang, Yang Yu, Chongjie Zhang Contextualized Scene Imagination for Generative Commonsense Reasoning
PeiFeng Wang, Jonathan Zamora, Junfeng Liu, Filip Ilievski, Muhao Chen, Xiang Ren Continuous-Time Meta-Learning with Forward Mode Differentiation
Tristan Deleu, David Kanaa, Leo Feng, Giancarlo Kerg, Yoshua Bengio, Guillaume Lajoie, Pierre-Luc Bacon Contrastive Clustering to Mine Pseudo Parallel Data for Unsupervised Translation
Xuan-Phi Nguyen, Hongyu Gong, Yun Tang, Changhan Wang, Philipp Koehn, Shafiq Joty Controlling Directions Orthogonal to a Classifier
Yilun Xu, Hao He, Tianxiao Shen, Tommi S. Jaakkola Convergent Graph Solvers
Junyoung Park, Jinhyun Choo, Jinkyoo Park Coordination Among Neural Modules Through a Shared Global Workspace
Anirudh Goyal, Aniket Rajiv Didolkar, Alex Lamb, Kartikeya Badola, Nan Rosemary Ke, Nasim Rahaman, Jonathan Binas, Charles Blundell, Michael Curtis Mozer, Yoshua Bengio COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
Jongmin Lee, Cosmin Paduraru, Daniel J Mankowitz, Nicolas Heess, Doina Precup, Kee-Eung Kim, Arthur Guez cosFormer: Rethinking SoftMax in Attention
Zhen Qin, Weixuan Sun, Hui Deng, Dongxu Li, Yunshen Wei, Baohong Lv, Junjie Yan, Lingpeng Kong, Yiran Zhong Creating Training Sets via Weak Indirect Supervision
Jieyu Zhang, Bohan Wang, Xiangchen Song, Yujing Wang, Yaming Yang, Jing Bai, Alexander Ratner Cross-Domain Imitation Learning via Optimal Transport
Arnaud Fickinger, Samuel Cohen, Stuart Russell, Brandon Amos Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL
Bogdan Mazoure, Ahmed M Ahmed, R Devon Hjelm, Andrey Kolobov, Patrick MacAlpine Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, Tommi S. Jaakkola CycleMLP: A MLP-like Architecture for Dense Prediction
Shoufa Chen, Enze Xie, Chongjian Ge, Runjian Chen, Ding Liang, Ping Luo DAB-DETR: Dynamic Anchor Boxes Are Better Queries for DETR
Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, Lei Zhang Data Poisoning Won’t Save You from Facial Recognition
Evani Radiya-Dixit, Sanghyun Hong, Nicholas Carlini, Florian Tramer Data-Driven Offline Optimization for Architecting Hardware Accelerators
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine Data-Efficient Graph Grammar Learning for Molecular Generation
Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik Declarative Nets That Are Equilibrium Models
Russell Tsuchida, Suk Yee Yong, Mohammad Ali Armin, Lars Petersson, Cheng Soon Ong Deconstructing the Inductive Biases of Hamiltonian Neural Networks
Nate Gruver, Marc Anton Finzi, Samuel Don Stanton, Andrew Gordon Wilson Decoupled Adaptation for Cross-Domain Object Detection
Junguang Jiang, Baixu Chen, Jianmin Wang, Mingsheng Long Deep Attentive Variational Inference
Ifigeneia Apostolopoulou, Ian Char, Elan Rosenfeld, Artur Dubrawski Deep AutoAugment
Yu Zheng, Zhi Zhang, Shen Yan, Mi Zhang Deep Ensembling with No Overhead for Either Training or Testing: The All-Round Blessings of Dynamic Sparsity
Shiwei Liu, Tianlong Chen, Zahra Atashgahi, Xiaohan Chen, Ghada Sokar, Elena Mocanu, Mykola Pechenizkiy, Zhangyang Wang, Decebal Constantin Mocanu Deep Point Cloud Reconstruction
Jaesung Choe, ByeongIn Joung, Francois Rameau, Jaesik Park, In So Kweon Defending Against Image Corruptions Through Adversarial Augmentations
Dan Andrei Calian, Florian Stimberg, Olivia Wiles, Sylvestre-Alvise Rebuffi, András György, Timothy A Mann, Sven Gowal DEGREE: Decomposition Based Explanation for Graph Neural Networks
Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang, Mengnan Du, Xia Hu Delaunay Component Analysis for Evaluation of Data Representations
Petra Poklukar, Vladislav Polianskii, Anastasiia Varava, Florian T. Pokorny, Danica Kragic Jensfelt DemoDICE: Offline Imitation Learning with Supplementary Imperfect Demonstrations
Geon-Hyeong Kim, Seokin Seo, Jongmin Lee, Wonseok Jeon, HyeongJoo Hwang, Hongseok Yang, Kee-Eung Kim Denoising Likelihood Score Matching for Conditional Score-Based Data Generation
Chen-Hao Chao, Wei-Fang Sun, Bo-Wun Cheng, Yi-Chen Lo, Chia-Che Chang, Yu-Lun Liu, Yu-Lin Chang, Chia-Ping Chen, Chun-Yi Lee DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting
Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu DictFormer: Tiny Transformer with Shared Dictionary
Qian Lou, Ting Hua, Yen-Chang Hsu, Yilin Shen, Hongxia Jin Differentiable DAG Sampling
Bertrand Charpentier, Simon Kibler, Stephan Günnemann Differentiable Prompt Makes Pre-Trained Language Models Better Few-Shot Learners
Ningyu Zhang, Luoqiu Li, Xiang Chen, Shumin Deng, Zhen Bi, Chuanqi Tan, Fei Huang, Huajun Chen Differentiable Scaffolding Tree for Molecule Optimization
Tianfan Fu, Wenhao Gao, Cao Xiao, Jacob Yasonik, Connor W. Coley, Jimeng Sun Differentially Private Fine-Tuning of Language Models
Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, Sergey Yekhanin, Huishuai Zhang Diffusion-Based Voice Conversion with Fast Maximum Likelihood Sampling Scheme
Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, Mikhail Sergeevich Kudinov, Jiansheng Wei Direct Then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching
Pierre-Alexandre Kamienny, Jean Tarbouriech, Sylvain Lamprier, Alessandro Lazaric, Ludovic Denoyer Discovering Invariant Rationales for Graph Neural Networks
Yingxin Wu, Xiang Wang, An Zhang, Xiangnan He, Tat-Seng Chua Discovering Latent Concepts Learned in BERT
Fahim Dalvi, Abdul Rafae Khan, Firoj Alam, Nadir Durrani, Jia Xu, Hassan Sajjad Discrepancy-Based Active Learning for Domain Adaptation
Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis Discrete Representations Strengthen Vision Transformer Robustness
Chengzhi Mao, Lu Jiang, Mostafa Dehghani, Carl Vondrick, Rahul Sukthankar, Irfan Essa DISSECT: Disentangled Simultaneous Explanations via Concept Traversals
Asma Ghandeharioun, Been Kim, Chun-Liang Li, Brendan Jou, Brian Eoff, Rosalind Picard Distribution Compression in Near-Linear Time
Abhishek Shetty, Raaz Dwivedi, Lester Mackey Distributional Reinforcement Learning with Monotonic Splines
Yudong Luo, Guiliang Liu, Haonan Duan, Oliver Schulte, Pascal Poupart DIVA: Dataset Derivative of a Learning Task
Yonatan Dukler, Alessandro Achille, Giovanni Paolini, Avinash Ravichandran, Marzia Polito, Stefano Soatto Diverse Client Selection for Federated Learning via Submodular Maximization
Ravikumar Balakrishnan, Tian Li, Tianyi Zhou, Nageen Himayat, Virginia Smith, Jeff Bilmes Do Deep Networks Transfer Invariances Across Classes?
Allan Zhou, Fahim Tajwar, Alexander Robey, Tom Knowles, George J. Pappas, Hamed Hassani, Chelsea Finn Do Users Benefit from Interpretable Vision? a User Study, Baseline, and Dataset
Leon Sixt, Martin Schuessler, Oana-Iuliana Popescu, Philipp Weiß, Tim Landgraf Do We Need Anisotropic Graph Neural Networks?
Shyam A. Tailor, Felix Opolka, Pietro Lio, Nicholas Donald Lane Does Your Graph Need a Confidence Boost? Convergent Boosted Smoothing on Graphs with Tabular Node Features
Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf Domain Adversarial Training: A Game Perspective
David Acuna, Marc T Law, Guojun Zhang, Sanja Fidler Domino: Discovering Systematic Errors with Cross-Modal Embeddings
Sabri Eyuboglu, Maya Varma, Khaled Kamal Saab, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, Christopher Re Doubly Adaptive Scaled Algorithm for Machine Learning Using Second-Order Information
Majid Jahani, Sergey Rusakov, Zheng Shi, Peter Richtárik, Michael W. Mahoney, Martin Takac DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization
Aviral Kumar, Rishabh Agarwal, Tengyu Ma, Aaron Courville, George Tucker, Sergey Levine Dropout Q-Functions for Doubly Efficient Reinforcement Learning
Takuya Hiraoka, Takahisa Imagawa, Taisei Hashimoto, Takashi Onishi, Yoshimasa Tsuruoka Dual Lottery Ticket Hypothesis
Yue Bai, Huan Wang, Zhiqiang Tao, Kunpeng Li, Yun Fu Dynamic Token Normalization Improves Vision Transformers
Wenqi Shao, Yixiao Ge, Zhaoyang Zhang, Xuyuan Xu, Xiaogang Wang, Ying Shan, Ping Luo Dynamics-Aware Comparison of Learned Reward Functions
Blake Wulfe, Logan Michael Ellis, Jean Mercat, Rowan Thomas McAllister, Adrien Gaidon Effective Model Sparsification by Scheduled Grow-and-Prune Methods
Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, Yuan Xie Efficient Self-Supervised Vision Transformers for Representation Learning
Chunyuan Li, Jianwei Yang, Pengchuan Zhang, Mei Gao, Bin Xiao, Xiyang Dai, Lu Yuan, Jianfeng Gao Efficient Sharpness-Aware Minimization for Improved Training of Neural Networks
Jiawei Du, Hanshu Yan, Jiashi Feng, Joey Tianyi Zhou, Liangli Zhen, Rick Siow Mong Goh, Vincent Tan Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators
John Guibas, Morteza Mardani, Zongyi Li, Andrew Tao, Anima Anandkumar, Bryan Catanzaro Emergent Communication at Scale
Rahma Chaabouni, Florian Strub, Florent Altché, Eugene Tarassov, Corentin Tallec, Elnaz Davoodi, Kory Wallace Mathewson, Olivier Tieleman, Angeliki Lazaridou, Bilal Piot Enabling Arbitrary Translation Objectives with Adaptive Tree Search
Wang Ling, Wojciech Stokowiec, Domenic Donato, Chris Dyer, Lei Yu, Laurent Sartran, Austin Matthews End-to-End Learning of Probabilistic Hierarchies on Graphs
Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann Energy-Inspired Molecular Conformation Optimization
Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng Environment Predictive Coding for Visual Navigation
Santhosh Kumar Ramakrishnan, Tushar Nagarajan, Ziad Al-Halah, Kristen Grauman Equivariant Graph Mechanics Networks with Constraints
Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, Junzhou Huang Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations
Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic Equivariant Subgraph Aggregation Networks
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, Haggai Maron Evaluating Model-Based Planning and Planner Amortization for Continuous Control
Arunkumar Byravan, Leonard Hasenclever, Piotr Trochim, Mehdi Mirza, Alessandro Davide Ialongo, Yuval Tassa, Jost Tobias Springenberg, Abbas Abdolmaleki, Nicolas Heess, Josh Merel, Martin Riedmiller Evidential Turing Processes
Melih Kandemir, Abdullah Akgül, Manuel Haussmann, Gozde Unal EViT: Expediting Vision Transformers via Token Reorganizations
Youwei Liang, Chongjian Ge, Zhan Tong, Yibing Song, Jue Wang, Pengtao Xie Explainable GNN-Based Models over Knowledge Graphs
David Jaime Tena Cucala, Bernardo Cuenca Grau, Egor V. Kostylev, Boris Motik Explaining Point Processes by Learning Interpretable Temporal Logic Rules
Shuang Li, Mingquan Feng, Lu Wang, Abdelmajid Essofi, Yufeng Cao, Junchi Yan, Le Song Explanations of Black-Box Models Based on Directional Feature Interactions
Aria Masoomi, Davin Hill, Zhonghui Xu, Craig P Hersh, Edwin K. Silverman, Peter J. Castaldi, Stratis Ioannidis, Jennifer Dy Exploiting Class Activation Value for Partial-Label Learning
Fei Zhang, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Tao Qin, Masashi Sugiyama Exploring Memorization in Adversarial Training
Yinpeng Dong, Ke Xu, Xiao Yang, Tianyu Pang, Zhijie Deng, Hang Su, Jun Zhu Exploring the Limits of Large Scale Pre-Training
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, Hanie Sedghi ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning
Vamsi Aribandi, Yi Tay, Tal Schuster, Jinfeng Rao, Huaixiu Steven Zheng, Sanket Vaibhav Mehta, Honglei Zhuang, Vinh Q. Tran, Dara Bahri, Jianmo Ni, Jai Gupta, Kai Hui, Sebastian Ruder, Donald Metzler Extending the WILDS Benchmark for Unsupervised Adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang F8Net: Fixed-Point 8-Bit Only Multiplication for Network Quantization
Qing Jin, Jian Ren, Richard Zhuang, Sumant Hanumante, Zhengang Li, Zhiyu Chen, Yanzhi Wang, Kaiyuan Yang, Sergey Tulyakov Fair Normalizing Flows
Mislav Balunovic, Anian Ruoss, Martin Vechev FairCal: Fairness Calibration for Face Verification
Tiago Salvador, Stephanie Cairns, Vikram Voleti, Noah Marshall, Adam M Oberman Fairness Guarantees Under Demographic Shift
Stephen Giguere, Blossom Metevier, Bruno Castro da Silva, Yuriy Brun, Philip S. Thomas, Scott Niekum Fast AdvProp
Jieru Mei, Yucheng Han, Yutong Bai, Yixiao Zhang, Yingwei Li, Xianhang Li, Alan Yuille, Cihang Xie Fast Model Editing at Scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D Manning Fast Regression for Structured Inputs
Raphael A Meyer, Cameron N Musco, Christopher P Musco, David Woodruff, Samson Zhou Fast Topological Clustering with Wasserstein Distance
Tananun Songdechakraiwut, Bryan M Krause, Matthew I Banks, Kirill V Nourski, Barry D Van Veen FastSHAP: Real-Time Shapley Value Estimation
Neil Jethani, Mukund Sudarshan, Ian Connick Covert, Su-In Lee, Rajesh Ranganath Few-Shot Backdoor Attacks on Visual Object Tracking
Yiming Li, Haoxiang Zhong, Xingjun Ma, Yong Jiang, Shu-Tao Xia FILIP: Fine-Grained Interactive Language-Image Pre-Training
Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, Chunjing Xu FILM: Following Instructions in Language with Modular Methods
So Yeon Min, Devendra Singh Chaplot, Pradeep Kumar Ravikumar, Yonatan Bisk, Ruslan Salakhutdinov Filtered-CoPhy: Unsupervised Learning of Counterfactual Physics in Pixel Space
Steeven Janny, Fabien Baradel, Natalia Neverova, Madiha Nadri, Greg Mori, Christian Wolf Finetuned Language Models Are Zero-Shot Learners
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, Quoc V Le Fixed Neural Network Steganography: Train the Images, Not the Network
Varsha Kishore, Xiangyu Chen, Yan Wang, Boyi Li, Kilian Q Weinberger FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
David W. Romero, Robert-Jan Bruintjes, Jakub Mikolaj Tomczak, Erik J Bekkers, Mark Hoogendoorn, Jan van Gemert Fooling Explanations in Text Classifiers
Adam Ivankay, Ivan Girardi, Chiara Marchiori, Pascal Frossard Fortuitous Forgetting in Connectionist Networks
Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron Courville Frame Averaging for Invariant and Equivariant Network Design
Omri Puny, Matan Atzmon, Edward J. Smith, Ishan Misra, Aditya Grover, Heli Ben-Hamu, Yaron Lipman Frequency-Aware SGD for Efficient Embedding Learning with Provable Benefits
Yan Li, Dhruv Choudhary, Xiaohan Wei, Baichuan Yuan, Bhargav Bhushanam, Tuo Zhao, Guanghui Lan GATSBI: Generative Adversarial Training for Simulation-Based Inference
Poornima Ramesh, Jan-Matthis Lueckmann, Jan Boelts, Álvaro Tejero-Cantero, David S. Greenberg, Pedro J. Goncalves, Jakob H. Macke Gaussian Mixture Convolution Networks
Adam Celarek, Pedro Hermosilla, Bernhard Kerbl, Timo Ropinski, Michael Wimmer GeneDisco: A Benchmark for Experimental Design in Drug Discovery
Arash Mehrjou, Ashkan Soleymani, Andrew Jesson, Pascal Notin, Yarin Gal, Stefan Bauer, Patrick Schwab Generalized Demographic Parity for Group Fairness
Zhimeng Jiang, Xiaotian Han, Chao Fan, Fan Yang, Ali Mostafavi, Xia Hu Generalized Kernel Thinning
Raaz Dwivedi, Lester Mackey Generalizing Few-Shot NAS with Gradient Matching
Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng Generating Videos with Dynamics-Aware Implicit Generative Adversarial Networks
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Ankesh Anand, Jacob C Walker, Yazhe Li, Eszter Vértes, Julian Schrittwieser, Sherjil Ozair, Theophane Weber, Jessica B Hamrick Proof Artifact Co-Training for Theorem Proving with Language Models
Jesse Michael Han, Jason Rute, Yuhuai Wu, Edward Ayers, Stanislas Polu Prospect Pruning: Finding Trainable Weights at Initialization Using Meta-Gradients
Milad Alizadeh, Shyam A. Tailor, Luisa M Zintgraf, Joost van Amersfoort, Sebastian Farquhar, Nicholas Donald Lane, Yarin Gal ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics
Boris N. Oreshkin, Florent Bocquelet, Felix G. Harvey, Bay Raitt, Dominic Laflamme Prototype Memory and Attention Mechanisms for Few Shot Image Generation
Tianqin Li, Zijie Li, Andrew Luo, Harold Rockwell, Amir Barati Farimani, Tai Sing Lee Provably Filtering Exogenous Distractors Using Multistep Inverse Dynamics
Yonathan Efroni, Dipendra Misra, Akshay Krishnamurthy, Alekh Agarwal, John Langford Provably Robust Adversarial Examples
Dimitar Iliev Dimitrov, Gagandeep Singh, Timon Gehr, Martin Vechev PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series
Paul Jeha, Michael Bohlke-Schneider, Pedro Mercado, Shubham Kapoor, Rajbir Singh Nirwan, Valentin Flunkert, Jan Gasthaus, Tim Januschowski Quadtree Attention for Vision Transformers
Shitao Tang, Jiahui Zhang, Siyu Zhu, Ping Tan Query Embedding on Hyper-Relational Knowledge Graphs
Dimitrios Alivanistos, Max Berrendorf, Michael Cochez, Mikhail Galkin R4D: Utilizing Reference Objects for Long-Range Distance Estimation
Yingwei Li, Tiffany Chen, Maya Kabkab, Ruichi Yu, Longlong Jing, Yurong You, Hang Zhao Real-Time Neural Voice Camouflage
Mia Chiquier, Chengzhi Mao, Carl Vondrick Recursive Disentanglement Network
Yixuan Chen, Yubin Shi, Dongsheng Li, Yujiang Wang, Mingzhi Dong, Yingying Zhao, Robert P. Dick, Qin Lv, Fan Yang, Li Shang Reinforcement Learning in Presence of Discrete Markovian Context Evolution
Hang Ren, Aivar Sootla, Taher Jafferjee, Junxiao Shen, Jun Wang, Haitham Bou Ammar Reinforcement Learning with Sparse Rewards Using Guidance from Offline Demonstration
Desik Rengarajan, Gargi Vaidya, Akshay Sarvesh, Dileep Kalathil, Srinivas Shakkottai Relational Surrogate Loss Learning
Tao Huang, Zekang Li, Hua Lu, Yong Shan, Shusheng Yang, Yang Feng, Fei Wang, Shan You, Chang Xu Reliable Adversarial Distillation with Unreliable Teachers
Jianing Zhu, Jiangchao Yao, Bo Han, Jingfeng Zhang, Tongliang Liu, Gang Niu, Jingren Zhou, Jianliang Xu, Hongxia Yang RelViT: Concept-Guided Vision Transformer for Visual Relational Reasoning
Xiaojian Ma, Weili Nie, Zhiding Yu, Huaizu Jiang, Chaowei Xiao, Yuke Zhu, Song-Chun Zhu, Anima Anandkumar Representation-Agnostic Shape Fields
Xiaoyang Huang, Jiancheng Yang, Yanjun Wang, Ziyu Chen, Linguo Li, Teng Li, Bingbing Ni, Wenjun Zhang Representational Continuity for Unsupervised Continual Learning
Divyam Madaan, Jaehong Yoon, Yuanchun Li, Yunxin Liu, Sung Ju Hwang Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings
Dongsheng Wang, Dan dan Guo, He Zhao, Huangjie Zheng, Korawat Tanwisuth, Bo Chen, Mingyuan Zhou Rethinking Class-Prior Estimation for Positive-Unlabeled Learning
Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Gang Niu, Masashi Sugiyama, Dacheng Tao Rethinking Goal-Conditioned Supervised Learning and Its Connection to Offline RL
Rui Yang, Yiming Lu, Wenzhe Li, Hao Sun, Meng Fang, Yali Du, Xiu Li, Lei Han, Chongjie Zhang Reverse Engineering of Imperceptible Adversarial Image Perturbations
Yifan Gong, Yuguang Yao, Yize Li, Yimeng Zhang, Xiaoming Liu, Xue Lin, Sijia Liu Revisiting Design Choices in Offline Model Based Reinforcement Learning
Cong Lu, Philip Ball, Jack Parker-Holder, Michael Osborne, Stephen J. Roberts Revisiting Over-Smoothing in BERT from the Perspective of Graph
Han Shi, Jiahui Gao, Hang Xu, Xiaodan Liang, Zhenguo Li, Lingpeng Kong, Stephen M. S. Lee, James Kwok Robust and Scalable SDE Learning: A Functional Perspective
Scott Alexander Cameron, Tyron Luke Cameron, Arnu Pretorius, Stephen J. Roberts Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?
Vikash Sehwag, Saeed Mahloujifar, Tinashe Handina, Sihui Dai, Chong Xiang, Mung Chiang, Prateek Mittal RvS: What Is Essential for Offline RL via Supervised Learning?
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, Sergey Levine Sample Selection with Uncertainty of Losses for Learning with Noisy Labels
Xiaobo Xia, Tongliang Liu, Bo Han, Mingming Gong, Jun Yu, Gang Niu, Masashi Sugiyama Scalable Sampling for Nonsymmetric Determinantal Point Processes
Insu Han, Mike Gartrell, Jennifer Gillenwater, Elvis Dohmatob, Amin Karbasi Scale Efficiently: Insights from Pretraining and Finetuning Transformers
Yi Tay, Mostafa Dehghani, Jinfeng Rao, William Fedus, Samira Abnar, Hyung Won Chung, Sharan Narang, Dani Yogatama, Ashish Vaswani, Donald Metzler Scaling Laws for Neural Machine Translation
Behrooz Ghorbani, Orhan Firat, Markus Freitag, Ankur Bapna, Maxim Krikun, Xavier Garcia, Ciprian Chelba, Colin Cherry Scene Transformer: A Unified Architecture for Predicting Future Trajectories of Multiple Agents
Jiquan Ngiam, Vijay Vasudevan, Benjamin Caine, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling, Rebecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, David J Weiss, Benjamin Sapp, Zhifeng Chen, Jonathon Shlens SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon Self-Joint Supervised Learning
Navid Kardan, Mubarak Shah, Mitch Hill Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis
Siyi Tang, Jared Dunnmon, Khaled Kamal Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel Rubin, Christopher Lee-Messer Semi-Relaxed Gromov-Wasserstein Divergence and Applications on Graphs
Cédric Vincent-Cuaz, Rémi Flamary, Marco Corneli, Titouan Vayer, Nicolas Courty SGD Can Converge to Local Maxima
Liu Ziyin, Botao Li, James B Simon, Masahito Ueda Shuffle Private Stochastic Convex Optimization
Albert Cheu, Matthew Joseph, Jieming Mao, Binghui Peng Signing the Supermask: Keep, Hide, Invert
Nils Koster, Oliver Grothe, Achim Rettinger Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond
Jonathan Godwin, Michael Schaarschmidt, Alexander L Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veličković, James Kirkpatrick, Peter Battaglia SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, Yuan Cao SketchODE: Learning Neural Sketch Representation in Continuous Time
Ayan Das, Yongxin Yang, Timothy Hospedales, Tao Xiang, Yi-Zhe Song Skill-Based Meta-Reinforcement Learning
Taewook Nam, Shao-Hua Sun, Karl Pertsch, Sung Ju Hwang, Joseph J Lim Sound Adversarial Audio-Visual Navigation
Yinfeng Yu, Wenbing Huang, Fuchun Sun, Changan Chen, Yikai Wang, Xiaohong Liu Space-Time Graph Neural Networks
Samar Hadou, Charilaos I Kanatsoulis, Alejandro Ribeiro Spanning Tree-Based Graph Generation for Molecules
Sungsoo Ahn, Binghong Chen, Tianzhe Wang, Le Song Sparse Attention with Learning to Hash
Zhiqing Sun, Yiming Yang, Shinjae Yoo Sparse Communication via Mixed Distributions
António Farinhas, Wilker Aziz, Vlad Niculae, Andre Martins Sparsity Winning Twice: Better Robust Generalization from More Efficient Training
Tianlong Chen, Zhenyu Zhang, Pengjun Wang, Santosh Balachandra, Haoyu Ma, Zehao Wang, Zhangyang Wang Spatial Graph Attention and Curiosity-Driven Policy for Antiviral Drug Discovery
Yulun Wu, Nicholas Choma, Andrew Deru Chen, Mikaela Cashman, Erica Teixeira Prates, Veronica G Melesse Vergara, Manesh B Shah, Austin Clyde, Thomas Brettin, Wibe Albert de Jong, Neeraj Kumar, Martha S Head, Rick L. Stevens, Peter Nugent, Daniel A Jacobson, James B Brown Spherical Message Passing for 3D Molecular Graphs
Yi Liu, Limei Wang, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, Shuiwang Ji SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
Cong Guo, Yuxian Qiu, Jingwen Leng, Xiaotian Gao, Chen Zhang, Yunxin Liu, Fan Yang, Yuhao Zhu, Minyi Guo Stein Latent Optimization for Generative Adversarial Networks
Uiwon Hwang, Heeseung Kim, Dahuin Jung, Hyemi Jang, Hyungyu Lee, Sungroh Yoon Step-Unrolled Denoising Autoencoders for Text Generation
Nikolay Savinov, Junyoung Chung, Mikolaj Binkowski, Erich Elsen, Aaron van den Oord Stochastic Training Is Not Necessary for Generalization
Jonas Geiping, Micah Goldblum, Phil Pope, Michael Moeller, Tom Goldstein Strength of Minibatch Noise in SGD
Liu Ziyin, Kangqiao Liu, Takashi Mori, Masahito Ueda Subspace Regularizers for Few-Shot Class Incremental Learning
Afra Feyza Akyürek, Ekin Akyürek, Derry Wijaya, Jacob Andreas Superclass-Conditional Gaussian Mixture Model for Learning Fine-Grained Embeddings
Jingchao Ni, Wei Cheng, Zhengzhang Chen, Takayoshi Asakura, Tomoya Soma, Sho Kato, Haifeng Chen Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-Training Paradigm
Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, Junjie Yan Surrogate Gap Minimization Improves Sharpness-Aware Training
Juntang Zhuang, Boqing Gong, Liangzhe Yuan, Yin Cui, Hartwig Adam, Nicha C Dvornek, Sekhar Tatikonda, James s Duncan, Ting Liu Surrogate NAS Benchmarks: Going Beyond the Limited Search Spaces of Tabular NAS Benchmarks
Arber Zela, Julien Niklas Siems, Lucas Zimmer, Jovita Lukasik, Margret Keuper, Frank Hutter Switch-GLAT: Multilingual Parallel Machine Translation via Code-Switch Decoder
Zhenqiao Song, Hao Zhou, Lihua Qian, Jingjing Xu, Shanbo Cheng, Mingxuan Wang, Lei Li Synchromesh: Reliable Code Generation from Pre-Trained Language Models
Gabriel Poesia, Alex Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, Sumit Gulwani TAda! Temporally-Adaptive Convolutions for Video Understanding
Ziyuan Huang, Shiwei Zhang, Liang Pan, Zhiwu Qing, Mingqian Tang, Ziwei Liu, Marcelo H Ang Jr Taming Sparsely Activated Transformer with Stochastic Experts
Simiao Zuo, Xiaodong Liu, Jian Jiao, Young Jin Kim, Hany Hassan, Ruofei Zhang, Jianfeng Gao, Tuo Zhao TAPEX: Table Pre-Training via Learning a Neural SQL Executor
Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou Target-Side Input Augmentation for Sequence to Sequence Generation
Shufang Xie, Ang Lv, Yingce Xia, Lijun Wu, Tao Qin, Tie-Yan Liu, Rui Yan Task-Induced Representation Learning
Jun Yamada, Karl Pertsch, Anisha Gunjal, Joseph J Lim The Efficiency Misnomer
Mostafa Dehghani, Yi Tay, Anurag Arnab, Lucas Beyer, Ashish Vaswani The MultiBERTs: BERT Reproductions for Robustness Analysis
Thibault Sellam, Steve Yadlowsky, Ian Tenney, Jason Wei, Naomi Saphra, Alexander D'Amour, Tal Linzen, Jasmijn Bastings, Iulia Raluca Turc, Jacob Eisenstein, Dipanjan Das, Ellie Pavlick The Role of Pretrained Representations for the OOD Generalization of RL Agents
Frederik Träuble, Andrea Dittadi, Manuel Wuthrich, Felix Widmaier, Peter Vincent Gehler, Ole Winther, Francesco Locatello, Olivier Bachem, Bernhard Schölkopf, Stefan Bauer The Spectral Bias of Polynomial Neural Networks
Moulik Choraria, Leello Tadesse Dadi, Grigorios Chrysos, Julien Mairal, Volkan Cevher The Uncanny Similarity of Recurrence and Depth
Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
Shiwei Liu, Tianlong Chen, Xiaohan Chen, Li Shen, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy THOMAS: Trajectory Heatmap Output with Learned Multi-Agent Sampling
Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou, Bogdan Stanciulescu, Fabien Moutarde Topological Experience Replay
Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal Topological Graph Neural Networks
Max Horn, Edward De Brouwer, Michael Moor, Yves Moreau, Bastian Rieck, Karsten Borgwardt Topologically Regularized Data Embeddings
Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys Towards a Unified View of Parameter-Efficient Transfer Learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, Graham Neubig Towards Continual Knowledge Learning of Language Models
Joel Jang, Seonghyeon Ye, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, Stanley Jungkyu Choi, Minjoon Seo Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations
Anuroop Sriram, Abhishek Das, Brandon M Wood, Siddharth Goyal, C. Lawrence Zitnick Transformer-Based Transform Coding
Yinhao Zhu, Yang Yang, Taco Cohen Transformers Can Do Bayesian Inference
Samuel Müller, Noah Hollmann, Sebastian Pineda Arango, Josif Grabocka, Frank Hutter Triangle and Four Cycle Counting with Predictions in Graph Streams
Justin Y Chen, Talya Eden, Piotr Indyk, Honghao Lin, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, Tal Wagner, David Woodruff, Michael Zhang Trigger Hunting with a Topological Prior for Trojan Detection
Xiaoling Hu, Xiao Lin, Michael Cogswell, Yi Yao, Susmit Jha, Chao Chen Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
Jakub Grudzien Kuba, Ruiqing Chen, Muning Wen, Ying Wen, Fanglei Sun, Jun Wang, Yaodong Yang Uncertainty Modeling for Out-of-Distribution Generalization
Xiaotong Li, Yongxing Dai, Yixiao Ge, Jun Liu, Ying Shan, Lingyu Duan Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
Yongqiang Chen, Han Yang, Yonggang Zhang, Ma Kaili, Tongliang Liu, Bo Han, James Cheng Understanding and Leveraging Overparameterization in Recursive Value Estimation
Chenjun Xiao, Bo Dai, Jincheng Mei, Oscar A Ramirez, Ramki Gummadi, Chris Harris, Dale Schuurmans Understanding Over-Squashing and Bottlenecks on Graphs via Curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein Understanding the Variance Collapse of SVGD in High Dimensions
Jimmy Ba, Murat A Erdogdu, Marzyeh Ghassemi, Shengyang Sun, Taiji Suzuki, Denny Wu, Tianzong Zhang Unified Visual Transformer Compression
Shixing Yu, Tianlong Chen, Jiayi Shen, Huan Yuan, Jianchao Tan, Sen Yang, Ji Liu, Zhangyang Wang Universalizing Weak Supervision
Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Carl Roberts, Frederic Sala Unrolling PALM for Sparse Semi-Blind Source Separation
Mohammad Fahes, Christophe Kervazo, Jérôme Bobin, Florence Tupin Unsupervised Semantic Segmentation by Distilling Feature Correspondences
Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, William T. Freeman VAE Approximation Error: ELBO and Exponential Families
Alexander Shekhovtsov, Dmitrij Schlesinger, Boris Flach Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning
Dhruv Shah, Peng Xu, Yao Lu, Ted Xiao, Alexander T Toshev, Sergey Levine, Brian Ichter Value Gradient Weighted Model-Based Reinforcement Learning
Claas A Voelcker, Victor Liao, Animesh Garg, Amir-massoud Farahmand Variational Methods for Simulation-Based Inference
Manuel Glöckler, Michael Deistler, Jakob H. Macke Variational Neural Cellular Automata
Rasmus Berg Palm, Miguel González Duque, Shyam Sudhakaran, Sebastian Risi Variational Oracle Guiding for Reinforcement Learning
Dongqi Han, Tadashi Kozuno, Xufang Luo, Zhao-Yun Chen, Kenji Doya, Yuqing Yang, Dongsheng Li VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects
Ruihai Wu, Yan Zhao, Kaichun Mo, Zizheng Guo, Yian Wang, Tianhao Wu, Qingnan Fan, Xuelin Chen, Leonidas Guibas, Hao Dong Vector-Quantized Image Modeling with Improved VQGAN
Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, Yonghui Wu ViDT: An Efficient and Effective Fully Transformer-Based Object Detector
Hwanjun Song, Deqing Sun, Sanghyuk Chun, Varun Jampani, Dongyoon Han, Byeongho Heo, Wonjae Kim, Ming-Hsuan Yang Vision-Based Manipulators Need to Also See from Their Hands
Kyle Hsu, Moo Jin Kim, Rafael Rafailov, Jiajun Wu, Chelsea Finn Visual Correspondence Hallucination
Hugo Germain, Vincent Lepetit, Guillaume Bourmaud Visual Hyperacuity with Moving Sensor and Recurrent Neural Computations
Alexander Rivkind, Or Ram, Eldad Assa, Michael Kreiserman, Ehud Ahissar Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
Lukas Schott, Julius Von Kügelgen, Frederik Träuble, Peter Vincent Gehler, Chris Russell, Matthias Bethge, Bernhard Schölkopf, Francesco Locatello, Wieland Brendel ViTGAN: Training GANs with Vision Transformers
Kwonjoon Lee, Huiwen Chang, Lu Jiang, Han Zhang, Zhuowen Tu, Ce Liu W-CTC: A Connectionist Temporal Classification Loss with Wild Cards
Xingyu Cai, Jiahong Yuan, Yuchen Bian, Guangxu Xun, Jiaji Huang, Kenneth Church WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection
Liang Peng, Senbo Yan, Boxi Wu, Zheng Yang, Xiaofei He, Deng Cai Weighted Training for Cross-Task Learning
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Honglin Yuan, Warren Richard Morningstar, Lin Ning, Karan Singhal What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization
Maximilian Böther, Otto Kißig, Martin Taraz, Sarel Cohen, Karen Seidel, Tobias Friedrich When Should Agents Explore?
Miruna Pislar, David Szepesvari, Georg Ostrovski, Diana L Borsa, Tom Schaul When, Why, and Which Pretrained GANs Are Useful?
Timofey Grigoryev, Andrey Voynov, Artem Babenko Why Propagate Alone? Parallel Use of Labels and Features on Graphs
Yangkun Wang, Jiarui Jin, Weinan Zhang, Yang Yongyi, Jiuhai Chen, Quan Gan, Yong Yu, Zheng Zhang, Zengfeng Huang, David Wipf Wisdom of Committees: An Overlooked Approach to Faster and More Accurate Models
Xiaofang Wang, Dan Kondratyuk, Eric Christiansen, Kris M. Kitani, Yair Movshovitz-Attias, Elad Eban Wish You Were Here: Hindsight Goal Selection for Long-Horizon Dexterous Manipulation
Todor Davchev, Oleg Olegovich Sushkov, Jean-Baptiste Regli, Stefan Schaal, Yusuf Aytar, Markus Wulfmeier, Jon Scholz You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
Osama Makansi, Julius Von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox, Bernhard Schölkopf Zero Pixel Directional Boundary by Vector Transform
Edoardo Mello Rella, Ajad Chhatkuli, Yun Liu, Ender Konukoglu, Luc Van Gool Zero-Shot Self-Supervised Learning for MRI Reconstruction
Burhaneddin Yaman, Seyed Amir Hossein Hosseini, Mehmet Akcakaya ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
Xinchi Qiu, Javier Fernandez-Marques, Pedro PB Gusmao, Yan Gao, Titouan Parcollet, Nicholas Donald Lane