Papers
11,015 papers found
Group-based Interleaved Pipeline Parallelism for Large-scale DNN Training
PengCheng Yang, Xiaoming Zhang, Wenpeng Zhang et al.
Group equivariant neural posterior estimation
Maximilian Dax, Stephen R Green, Jonathan Gair et al.
Half-Inverse Gradients for Physical Deep Learning
Patrick Schnell, Philipp Holl, Nils Thuerey
Handling Distribution Shifts on Graphs: An Invariance Perspective
Qitian Wu, Hengrui Zhang, Junchi Yan et al.
Heteroscedastic Temporal Variational Autoencoder For Irregularly Sampled Time Series
Satya Narayan Shukla, Benjamin Marlin
Hidden Convexity of Wasserstein GANs: Interpretable Generative Models with Closed-Form Solutions
Arda Sahiner, Tolga Ergen, Batu Ozturkler et al.
Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios
Vaisakh Shaj, Dieter Büchler, Rohit Sonker et al.
Hierarchical Few-Shot Imitation with Skill Transition Models
Kourosh Hakhamaneshi, Ruihan Zhao, Albert Zhan et al.
Hierarchical Variational Memory for Few-shot Learning Across Domains
Yingjun Du, Xiantong Zhen, Ling Shao et al.
High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize
Ali Kavis, Kfir Yehuda Levy, Volkan Cevher
High Probability Generalization Bounds with Fast Rates for Minimax Problems
Shaojie Li, Yong Liu
Hindsight Foresight Relabeling for Meta-Reinforcement Learning
Michael Wan, Jian Peng, Tanmay Gangwani
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
Yurong You, Katie Z Luo, Xiangyu Chen et al.
Hindsight: Posterior-guided training of retrievers for improved open-ended generation
Ashwin Paranjape, Omar Khattab, Christopher Potts et al.
Hot-Refresh Model Upgrades with Regression-Free Compatible Training in Image Retrieval
Binjie Zhang, Yixiao Ge, Yantao Shen et al.
How Attentive are Graph Attention Networks?
Shaked Brody, Uri Alon, Eran Yahav
How Did the Model Change? Efficiently Assessing Machine Learning API Shifts
Lingjiao Chen, Matei Zaharia, James Zou
How Does SimSiam Avoid Collapse Without Negative Samples? A Unified Understanding with Self-supervised Contrastive Learning
Chaoning Zhang, Kang Zhang, Chenshuang Zhang et al.
How Do Vision Transformers Work?
Namuk Park, Songkuk Kim
How Low Can We Go: Trading Memory for Error in Low-Precision Training
Chengrun Yang, Ziyang Wu, Jerry Chee et al.
How many degrees of freedom do we need to train deep networks: a loss landscape perspective
Brett W Larsen, Stanislav Fort, Nic Becker et al.
How Much Can CLIP Benefit Vision-and-Language Tasks?
Sheng Shen, Liunian Harold Li, Hao Tan et al.
How to deal with missing data in supervised deep learning?
Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data
Zhiyuan Zhang, Lingjuan Lyu, Weiqiang Wang et al.
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective
Yimeng Zhang, Yuguang Yao, Jinghan Jia et al.