Papers
11,015 papers found
Graph Information Bottleneck for Subgraph Recognition
Junchi Yu, Tingyang Xu, Yu Rong et al.
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning
Elan Sopher Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri et al.
GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing
Tao Yu, Chien-Sheng Wu, Xi Victoria Lin et al.
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity
Shaocong Ma, Ziyi Chen, Yi Zhou et al.
Grounded Language Learning Fast and Slow
Felix Hill, Olivier Tieleman, Tamara von Glehn et al.
Grounding Language to Autonomously-Acquired Skills via Goal Generation
Ahmed Akakzia, Cédric Colas, Pierre-Yves Oudeyer et al.
Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning
Zhenfang Chen, Jiayuan Mao, Jiajun Wu et al.
Group Equivariant Conditional Neural Processes
Makoto Kawano, Wataru Kumagai, Akiyoshi Sannai et al.
Group Equivariant Generative Adversarial Networks
Neel Dey, Antong Chen, Soheil Ghafurian
Group Equivariant Stand-Alone Self-Attention For Vision
David W. Romero, Jean-Baptiste Cordonnier
Growing Efficient Deep Networks by Structured Continuous Sparsification
Xin Yuan, Pedro Henrique Pamplona Savarese, Michael Maire
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu et al.
HalentNet: Multimodal Trajectory Forecasting with Hallucinative Intents
Deyao Zhu, Mohamed Zahran, Li Erran Li et al.
Heating up decision boundaries: isocapacitory saturation, adversarial scenarios and generalization bounds
Bogdan Georgiev, Lukas Franken, Mayukh Mukherjee
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
Enmao Diao, Jie Ding, Vahid Tarokh
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization
Kaidi Cao, Yining Chen, Junwei Lu et al.
Hierarchical Autoregressive Modeling for Neural Video Compression
Ruihan Yang, Yibo Yang, Joseph Marino et al.
Hierarchical Reinforcement Learning by Discovering Intrinsic Options
Jesse Zhang, Haonan Yu, Wei Xu
High-Capacity Expert Binary Networks
Adrian Bulat, Brais Martinez, Georgios Tzimiropoulos
Hopfield Networks is All You Need
Hubert Ramsauer, Bernhard Schäfl, Johannes Lehner et al.
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning
Honglu Zhou, Asim Kadav, Farley Lai et al.
How Benign is Benign Overfitting ?
Amartya Sanyal, Puneet K. Dokania, Varun Kanade et al.
How Does Mixup Help With Robustness and Generalization?
Linjun Zhang, Zhun Deng, Kenji Kawaguchi et al.
How Much Over-parameterization Is Sufficient to Learn Deep ReLU Networks?
Zixiang Chen, Yuan Cao, Difan Zou et al.
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks
Keyulu Xu, Mozhi Zhang, Jingling Li et al.