General Keywords

[ Algorithms ] [ Algorithms; Optimization ] [ Applications ] [ Data, Challenges, Implementations, and Software ] [ Deep Learning ] [ Deep Learning; Deep Learning ] [ Neuroscience and Cognitive Science ] [ Optimization ] [ Optimization; Optimization ] [ Probabilistic Methods ] [ Probabilistic Methods; Probabilistic Methods ] [ Reinforcement Learning and Planning ] [ Social Aspects of Machine Learning ] [ Theory ] [ Theory; Theory ]

Topic Keywords

[ Active Learning ] [ Active Learning; Algorithms ] [ Activity and Event Recognition ] [ Adaptive Data Analysis; Optimization ] [ Adversarial Examples ] [ Adversarial Learning ] [ Adversarial Learning; Algorithms ] [ Adversarial Networks ] [ Adversarial Networks ] [ Adversarial Networks; Deep Learning ] [ Adversarial Networks; Deep Learning ] [ AI Safety ] [ Algorithms Evaluation ] [ Approximate Inference ] [ Architectures ] [ Attention Models ] [ Audio and Speech Processing ] [ AutoML ] [ Bandit Algorithms ] [ Bandit Algorithms; Algorithms ] [ Bandit Algorithms; Reinforcement Learning and Planning ] [ Bandit Algorithms; Reinforcement Learning and Planning ] [ Bandits ] [ Bayesian Deep Learning ] [ Bayesian Methods ] [ Bayesian Nonparametrics ] [ Bayesian Theory ] [ Bayesian Theory ] [ Benchmarks ] [ Biologically Plausible Deep Networks ] [ Biologically Plausible Deep Networks; Deep Learning ] [ Biologically Plausible Deep Networks; Neuroscience and Cognitive Science ] [ Body Pose, Face, and Gesture Analysis ] [ Body Pose, Face, and Gesture Analysis; Applications ] [ Boosting and Ensemble Methods ] [ Boosting and Ensemble Methods; Algorithms ] [ Boosting and Ensemble Methods; Probabilistic Methods; Probabilistic Methods ] [ Causal Inference ] [ Classification ] [ Classification; Algorithms ] [ Classification; Algorithms ] [ Classification; Applications ] [ Classification; Deep Learning; Deep Learning ] [ Classification; Deep Learning; Deep Learning ] [ Clustering ] [ Clustering; Applications ] [ Clustering; Theory ] [ CNN Architectures; Deep Learning ] [ CNN Architectures; Deep Learning ] [ CNN Architectures; Theory ] [ Cognitive Science; Neuroscience and Cognitive Science ] [ Collaborative Filtering ] [ Collaborative Filtering; Algorithms ] [ Collaborative Filtering; Applications ] [ Combinatorial Optimization ] [ Components Analysis (e.g., CCA, ICA, LDA, PCA) ] [ Computational Biology and Bioinformatics ] [ Computational Biology and Bioinformatics; Applications ] [ Computational Complexity ] [ Computational Learning Theory ] [ Computational Photography ] [ Computational Social Science ] [ Computer Vision ] [ Computer Vision; Applications ] [ Computer Vision; Applications ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Computer Vision; Deep Learning ] [ Continual Learning ] [ Convex Optimization ] [ Convex Optimization; Optimization ] [ Convex Optimization; Probabilistic Methods; Theory; Theory ] [ Convex Optimization; Theory ] [ Crowdsourcing ] [ Decision and Control ] [ Deep Autoencoders; Deep Learning ] [ Deep learning Theory ] [ Deep RL ] [ Density Estimation ] [ Density Estimation; Deep Learning ] [ Derivative Free Optimization ] [ Dialog- or Communication-Based Learning ] [ Dimensionality Reduction ] [ Distributed and Parallel Optimization ] [ Distributed Inference ] [ Efficient Inference Methods ] [ Efficient Training Methods; Deep Learning ] [ Embedding and Representation learning ] [ Embedding Approaches ] [ Exploration ] [ Fairness, Accountability, and Transparency ] [ Fairness, Accountability, and Transparency ] [ Few-Shot Learning ] [ Few-Shot Learning; Algorithms ] [ Frequentist Statistics ] [ Game Theory and Computational Economics ] [ Gaussian Processes ] [ Gaussian Processes and Bayesian non-parametrics ] [ Generative Models ] [ Generative Models ] [ Graphical Models ] [ Graphical Models ] [ Hardware and Systems ] [ Healthcare ] [ Human or Animal Learning ] [ Human or Animal Learning; Probabilistic Methods ] [ Image Segmentation ] [ Image Segmentation; Algorithms ] [ Image Segmentation; Applications ] [ Information Theory ] [ Kernel Methods ] [ Kernel Methods; Optimization ] [ Large Deviations and Asymptotic Analysis ] [ Large Scale Learning ] [ Large Scale Learning; Algorithms ] [ Large Scale Learning; Algorithms ] [ Large Scale Learning; Applications ] [ Large Scale Learning; Deep Learning ] [ Large Scale Learning; Probabilistic Methods ] [ Latent Variable Models ] [ Learning Theory ] [ Markov Decision Processes ] [ Markov Decision Processes; Reinforcement Learning and Planning ] [ Markov Decision Processes; Reinforcement Learning and Planning ] [ Matrix and Tensor Factorization ] [ MCMC ] [ Memory ] [ Memory; Optimization ] [ Meta-Learning ] [ Meta-Learning; Applications ] [ Metric Learning ] [ Missing Data; Algorithms ] [ Missing Data; Algorithms ] [ Missing Data; Theory ] [ Model Selection and Structure Learning ] [ Models of Learning and Generalization ] [ Monte Carlo Methods ] [ Multi-Agent RL ] [ Multimodal Learning ] [ Multitask and Transfer Learning ] [ Multitask and Transfer Learning; Algorithms ] [ Multitask and Transfer Learning; Probabilistic Methods ] [ Multitask, Transfer, and Meta Learning ] [ Natural Language Processing ] [ Network Analysis ] [ Networks and Relational Learning ] [ Neural Coding; Neuroscience and Cognitive Science ] [ Neuroscience ] [ Neuroscience and Cognitive Science ] [ Non-Convex Optimization ] [ Non-Convex Optimization ] [ Non-Convex Optimization; Theory ] [ Non-parametric models ] [ Object Detection; Deep Learning ] [ Object Detection; Neuroscience and Cognitive Science ] [ Online Learning ] [ Online Learning Algorithms ] [ Online Learning Theory ] [ Online Learning; Theory ] [ Optimal Transport ] [ Optimization for Deep Networks ] [ Others ] [ Others ] [ Others ] [ Others ] [ Others ] [ Planning and Control ] [ Plasticity and Adaptation ] [ Predictive Models ] [ Predictive Models; Deep Learning ] [ Predictive Models; Deep Learning ] [ Privacy, Anonymity, and Security ] [ Privacy, Anonymity, and Security ] [ Probabilistic Methods ] [ Probabilistic Programming ] [ Program Understanding and Generation ] [ Quantitative Finance and Econometrics ] [ Ranking and Preference Learning ] [ Ranking and Preference Learning; Theory ] [ Reasoning; Optimization ] [ Recommender Systems ] [ Recurrent Networks ] [ Recurrent Networks; Theory ] [ Regression ] [ Regression; Algorithms ] [ Regression; Applications ] [ Regression; Optimization ] [ Regression; Probabilistic Methods; Probabilistic Methods ] [ Regularization ] [ Regularization ] [ Reinforcement Learning ] [ Reinforcement Learning and Planning ] [ Relational Learning ] [ Representation Learning ] [ Representation Learning; Algorithms ] [ Representation Learning; Algorithms ] [ Representation Learning; Neuroscience and Cognitive Science ] [ Representation Learning; Neuroscience and Cognitive Science; Neuroscience and Cognitive Science ] [ Representation Learning; Optimization ] [ RL, Decisions and Control Theory ] [ Robotics ] [ Robust statistics ] [ Semi-Supervised Learning ] [ Social Aspects of Machine Learning ] [ Software Toolkits ] [ Spaces of Functions and Kernels ] [ Sparse Coding and Dimensionality Expansion; Applications ] [ Sparsity and Compressed Sensing ] [ Sparsity and Compressed Sensing; Applications ] [ Sparsity and Compressed Sensing; Optimization; Theory ] [ Speech Recognition ] [ Statistical Learning Theory ] [ Statistical Physics of Learning ] [ Stochastic Optimization ] [ Structured Prediction ] [ Submodular Optimization ] [ Supervised Learning ] [ Sustainability and Environment ] [ Theory ] [ Time Series Analysis ] [ Time Series Analysis; Deep Learning ] [ Time Series Analysis; Probabilistic Methods; Probabilistic Methods ] [ Time Series and Sequences ] [ Topic Models ] [ Uncertainty Estimation ] [ Uncertainty Estimation; Applications; Probabilistic Methods ] [ Unsupervised Learning ] [ Unsupervised Learning; Applications ] [ Unsupervised Learning; Deep Learning ] [ Variational Inference ] [ Visualization or Exposition Techniques for Deep Networks ] [ Visual Question Answering ] [ Visual Scene Analysis and Interpretation ]

67 Results

Affinity Workshop
Mon 15:20 GAN-based Data Mapping for Model Adaptation
Leno Silva, Ruben Glatt, Renato Vicente
Oral
Tue 5:00 Relative Positional Encoding for Transformers with Linear Complexity
Antoine Liutkus, Ondřej Cífka, Shih-Lun Wu, Umut Simsekli, Yi-Hsuan Yang, Gaël RICHARD
Oral
Tue 5:00 Attention is not all you need: pure attention loses rank doubly exponentially with depth
Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas
Spotlight
Tue 5:20 A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
Yuhang Li, Shikuang Deng, Xin Dong, Ruihao Gong, Shi Gu
Spotlight
Tue 5:25 A Unified Lottery Ticket Hypothesis for Graph Neural Networks
Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, Zhangyang Wang
Spotlight
Tue 5:30 Generative Adversarial Transformers
Drew A. Hudson, Larry Zitnick
Spotlight
Tue 5:35 Evolving Attention with Residual Convolutions
Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, JING YU, Ce Zhang, Gao Huang, Yunhai Tong
Spotlight
Tue 5:40 Zoo-Tuning: Adaptive Transfer from A Zoo of Models
Yang Shu, Zhi Kou, Zhangjie Cao, Jianmin Wang, Mingsheng Long
Spotlight
Tue 5:45 UnICORNN: A recurrent model for learning very long time dependencies
T. Konstantin Rusch, Siddhartha Mishra
Oral
Tue 6:00 Neural Architecture Search without Training
Joe Mellor, Jack Turner, Amos Storkey, Elliot Crowley
Oral
Tue 6:00 Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework
Floris Geerts, Filip Mazowiecki, Guillermo Perez
Spotlight
Tue 6:20 Is Space-Time Attention All You Need for Video Understanding?
Gedas Bertasius, Heng Wang, Lorenzo Torresani
Spotlight
Tue 6:30 KNAS: Green Neural Architecture Search
Jingjing Xu, Liang Zhao, Junyang Lin, Rundong Gao, Xu SUN, Hongxia Yang
Spotlight
Tue 6:35 Efficient Lottery Ticket Finding: Less Data is More
Zhenyu Zhang, Xuxi Chen, Tianlong Chen, Zhangyang Wang
Spotlight
Tue 6:40 ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
Stéphane d'Ascoli, Hugo Touvron, Matthew Leavitt, Ari Morcos, Giulio Biroli, Levent Sagun
Spotlight
Tue 6:45 Provably Strict Generalisation Benefit for Equivariant Models
Bryn Elesedy, Sheheryar Zaidi
Oral
Tue 7:00 Not All Memories are Created Equal: Learning to Forget by Expiring
Sainbayar Sukhbaatar, Dexter JU, Spencer Poff, Stephen Roller, Arthur Szlam, Jason Weston, Angela Fan
Oral
Tue 7:00 OmniNet: Omnidirectional Representations from Transformers
Yi Tay, Mostafa Dehghani, Vamsi Aribandi, Jai Gupta, Philip Pham, Zhen Qin, Dara Bahri, Da-Cheng Juan, Don Metzler
Spotlight
Tue 7:20 Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size
Jack Kosaian, Amar Phanishayee, Matthai Philipose, Debadeepta Dey, Rashmi Vinayak
Spotlight
Tue 7:25 Perceiver: General Perception with Iterative Attention
Andrew Jaegle, Felix Axel Gimeno Gil, Andy Brock, Oriol Vinyals, Andrew Zisserman, Joao Carreira
Spotlight
Tue 7:30 Synthesizer: Rethinking Self-Attention for Transformer Models
Yi Tay, Dara Bahri, Don Metzler, Da-Cheng Juan, Zhe Zhao, Che Zheng
Spotlight
Tue 7:30 Grid-Functioned Neural Networks
Javier Dehesa, Andrew Vidler, Julian Padget, Christof Lutteroth
Spotlight
Tue 7:40 Parallelizing Legendre Memory Unit Training
Narsimha Reddy Chilkuri, Chris Eliasmith
Poster
Tue 9:00 Parallelizing Legendre Memory Unit Training
Narsimha Reddy Chilkuri, Chris Eliasmith
Poster
Tue 9:00 Evolving Attention with Residual Convolutions
Yujing Wang, Yaming Yang, Jiangang Bai, Mingliang Zhang, Jing Bai, JING YU, Ce Zhang, Gao Huang, Yunhai Tong
Poster
Tue 9:00 Not All Memories are Created Equal: Learning to Forget by Expiring
Sainbayar Sukhbaatar, Dexter JU, Spencer Poff, Stephen Roller, Arthur Szlam, Jason Weston, Angela Fan
Poster
Tue 9:00 Is Space-Time Attention All You Need for Video Understanding?
Gedas Bertasius, Heng Wang, Lorenzo Torresani
Poster
Tue 9:00 Generative Adversarial Transformers
Drew A. Hudson, Larry Zitnick
Poster
Tue 9:00 Relative Positional Encoding for Transformers with Linear Complexity
Antoine Liutkus, Ondřej Cífka, Shih-Lun Wu, Umut Simsekli, Yi-Hsuan Yang, Gaël RICHARD
Poster
Tue 9:00 Boosting the Throughput and Accelerator Utilization of Specialized CNN Inference Beyond Increasing Batch Size
Jack Kosaian, Amar Phanishayee, Matthai Philipose, Debadeepta Dey, Rashmi Vinayak
Poster
Tue 9:00 A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration
Yuhang Li, Shikuang Deng, Xin Dong, Ruihao Gong, Shi Gu
Poster
Tue 9:00 KNAS: Green Neural Architecture Search
Jingjing Xu, Liang Zhao, Junyang Lin, Rundong Gao, Xu SUN, Hongxia Yang
Poster
Tue 9:00 Neural Architecture Search without Training
Joe Mellor, Jack Turner, Amos Storkey, Elliot Crowley
Poster
Tue 9:00 Grid-Functioned Neural Networks
Javier Dehesa, Andrew Vidler, Julian Padget, Christof Lutteroth
Poster
Tue 9:00 OmniNet: Omnidirectional Representations from Transformers
Yi Tay, Mostafa Dehghani, Vamsi Aribandi, Jai Gupta, Philip Pham, Zhen Qin, Dara Bahri, Da-Cheng Juan, Don Metzler
Poster
Tue 9:00 Synthesizer: Rethinking Self-Attention for Transformer Models
Yi Tay, Dara Bahri, Don Metzler, Da-Cheng Juan, Zhe Zhao, Che Zheng
Poster
Tue 9:00 UnICORNN: A recurrent model for learning very long time dependencies
T. Konstantin Rusch, Siddhartha Mishra
Poster
Tue 9:00 ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
Stéphane d'Ascoli, Hugo Touvron, Matthew Leavitt, Ari Morcos, Giulio Biroli, Levent Sagun
Poster
Tue 9:00 Attention is not all you need: pure attention loses rank doubly exponentially with depth
Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas
Poster
Tue 9:00 Zoo-Tuning: Adaptive Transfer from A Zoo of Models
Yang Shu, Zhi Kou, Zhangjie Cao, Jianmin Wang, Mingsheng Long
Poster
Tue 9:00 Perceiver: General Perception with Iterative Attention
Andrew Jaegle, Felix Axel Gimeno Gil, Andy Brock, Oriol Vinyals, Andrew Zisserman, Joao Carreira
Poster
Tue 9:00 Provably Strict Generalisation Benefit for Equivariant Models
Bryn Elesedy, Sheheryar Zaidi
Poster
Tue 9:00 Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework
Floris Geerts, Filip Mazowiecki, Guillermo Perez
Poster
Tue 9:00 A Unified Lottery Ticket Hypothesis for Graph Neural Networks
Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, Zhangyang Wang
Poster
Tue 9:00 Efficient Lottery Ticket Finding: Less Data is More
Zhenyu Zhang, Xuxi Chen, Tianlong Chen, Zhangyang Wang
Oral
Tue 17:00 A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
Marc Finzi, Max Welling, Andrew Wilson
Spotlight
Tue 17:35 Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof T Schütt, Oliver Unke, Michael Gastegger
Spotlight
Tue 17:40 Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies.
Denis Lukovnikov, Asja Fischer
Oral
Tue 19:00 AlphaNet: Improved Training of Supernets with Alpha-Divergence
Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra
Spotlight
Tue 19:20 Catformer: Designing Stable Transformers via Sensitivity Analysis
Jared Quincy Davis, Albert Gu, Krzysztof Choromanski, Tri Dao, Christopher Re, Chelsea Finn, Percy Liang
Spotlight
Tue 19:25 A Receptor Skeleton for Capsule Neural Networks
Jintai Chen, Hongyun Yu, Chengde Qian, Danny Z Chen, Jian Wu
Spotlight
Tue 19:35 K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
Xiu Su, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, Changshui Zhang, Chang Xu
Poster
Tue 21:00 Catformer: Designing Stable Transformers via Sensitivity Analysis
Jared Quincy Davis, Albert Gu, Krzysztof Choromanski, Tri Dao, Christopher Re, Chelsea Finn, Percy Liang
Poster
Tue 21:00 Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof T Schütt, Oliver Unke, Michael Gastegger
Poster
Tue 21:00 A Receptor Skeleton for Capsule Neural Networks
Jintai Chen, Hongyun Yu, Chengde Qian, Danny Z Chen, Jian Wu
Poster
Tue 21:00 AlphaNet: Improved Training of Supernets with Alpha-Divergence
Dilin Wang, Chengyue Gong, Meng Li, Qiang Liu, Vikas Chandra
Poster
Tue 21:00 Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies.
Denis Lukovnikov, Asja Fischer
Poster
Tue 21:00 A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups
Marc Finzi, Max Welling, Andrew Wilson
Poster
Tue 21:00 K-shot NAS: Learnable Weight-Sharing for NAS with K-shot Supernets
Xiu Su, Shan You, Mingkai Zheng, Fei Wang, Chen Qian, Changshui Zhang, Chang Xu
Workshop
Sat 12:09 SVP-CF: Selection via Proxy for Collaborative Filtering Data
Noveen Sachdeva, Julian McAuley, Carole-Jean Wu
Workshop
Long-term Cross Adversarial Training: A Robust Meta-learning Method for Few-shot Classification Tasks
FAN LIU, Shuyu Zhao, Xuelong Dai, Bin Xiao
Workshop
Adversarial Semantic Contour for Object Detection
Yichi Zhang, Zijian Zhu, Xiao Yang, Jun Zhu
Workshop
Defending Adversaries Using Unsupervised Feature Clustering VAE
Cheng Zhang, Pan Gao
Workshop
SVP-CF: Selection via Proxy for Collaborative Filtering Data
Noveen Sachdeva, Julian McAuley, Carole-Jean Wu
Workshop
Stateful Strategic Regression
Keegan Harris, Hoda Heidari, Steven Wu
Workshop
Finite-Sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime
Niladri Chatterji, Phil Long
Workshop
Towards Principled Disentanglement for Domain Generalization
Hanlin Zhang, Yi-Fan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric Xing