Papers
HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction
Lanxiang Xing, Haixu Wu, Yuezhou Ma et al.
Helpful or Harmful Data? Fine-tuning-free Shapley Attribution for Explaining Language Model Predictions
Jingtan Wang, Xiaoqiang Lin, Rui Qiao et al.
HexGen: Generative Inference of Large Language Model over Heterogeneous Environment
Youhe Jiang, Ran Yan, Xiaozhe Yao et al.
HGCN2SP: Hierarchical Graph Convolutional Network for Two-Stage Stochastic Programming
Yang Wu, Yifan Zhang, Zhenxing Liang et al.
Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural Networks
Arjun Karuvally, Terrence Sejnowski, Hava T Siegelmann
Hierarchical Integral Probability Metrics: A distance on random probability measures with low sample complexity
Marta Catalano, Hugo Lavenant
Hierarchical Neural Operator Transformer with Learnable Frequency-aware Loss Prior for Arbitrary-scale Super-resolution
Xihaier Luo, Xiaoning Qian, Byung-Jun Yoon
Hierarchical Novelty Detection via Fine-Grained Evidence Allocation
Spandan Pyakurel, Qi Yu
Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling
Raunaq Bhirangi, Chenyu Wang, Venkatesh Pattabiraman et al.
Hieros: Hierarchical Imagination on Structured State Space Sequence World Models
Paul Mattes, Rainer Schlosser, Ralf Herbrich
High-Dimensional Bayesian Optimization via Semi-Supervised Learning with Optimized Unlabeled Data Sampling
Yuxuan Yin, Yu Wang, Peng Li
High-Dimensional Geometric Streaming for Nearly Low Rank Data
Hossein Esfandiari, Praneeth Kacham, Vahab Mirrokni et al.
High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization
Yihang Chen, Fanghui Liu, Taiji Suzuki et al.
High-dimensional Linear Bandits with Knapsacks
Wanteng Ma, Dong Xia, Jiashuo Jiang
High-Order Contrastive Learning with Fine-grained Comparative Levels for Sparse Ordinal Tensor Completion
Yu Dai, Junchen Shen, Zijie Zhai et al.
High-Performance Temporal Reversible Spiking Neural Networks with $\mathcalO(L)$ Training Memory and $\mathcalO(1)$ Inference Cost
Jiakui Hu, Man Yao, Xuerui Qiu et al.
High-Probability Bound for Non-Smooth Non-Convex Stochastic Optimization with Heavy Tails
Langqi Liu, Yibo Wang, Lijun Zhang
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise
Eduard Gorbunov, Abdurakhmon Sadiev, Marina Danilova et al.
Highway Value Iteration Networks
Yuhui Wang, Weida Li, Francesco Faccio et al.
Homomorphism Counts for Graph Neural Networks: All About That Basis
Emily Jin, Michael M. Bronstein, Ismail Ilkan Ceylan et al.
How Deep Do We Need: Accelerating Training and Inference of Neural ODEs via Control Perspective
Keyan Miao, Konstantinos Gatsis
How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model
Umberto Maria Tomasini, Matthieu Wyart
How Does Goal Relabeling Improve Sample Efficiency?
Sirui Zheng, Chenjia Bai, Zhuoran Yang et al.
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
Ryan Liu, Theodore Sumers, Ishita Dasgupta et al.