Zhongkai Hao

According to our database, Zhongkai Hao authored at least 26 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning.

Chengyang Ying, Zhongkai Hao, Xinning Zhou, Xuezhou Xu, Hang Su, Xingxing Zhang, Jun Zhu

CoRR, 2024

Your Diffusion Model is Secretly a Certifiably Robust Classifier.

Huanran Chen, Yinpeng Dong, Shitong Shao, Zhongkai Hao, Xiao Yang, Hang Su, Jun Zhu

CoRR, 2024

Preconditioning for Physics-Informed Neural Networks.

Songming Liu, Chang Su, Jiachen Yao, Zhongkai Hao, Hang Su, Youjia Wu, Jun Zhu

CoRR, 2024

Improved Operator Learning by Orthogonal Attention.

Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng, Hang Su

Proceedings of the Forty-first International Conference on Machine Learning, 2024

PAPM: A Physics-aware Proxy Model for Process Systems.

Pengwei Liu, Zhongkai Hao, Xingyu Ren, Hangjie Yuan, Jiayang Ren, Dong Ni

Proceedings of the Forty-first International Conference on Machine Learning, 2024

DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training.

Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations.

Ze Cheng, Zhongkai Hao, Xiaoqiang Wang, Jianing Huang, Youjia Wu, Xudan Liu, Yiru Zhao, Songming Liu, Hang Su

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling.

Hong Wang, Zhongkai Hao, Jie Wang, Zijie Geng, Zhen Wang, Bin Li, Feng Wu

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs.

Zhongkai Hao, Jiachen Yao, Chang Su, Hang Su, Ziao Wang, Fanzhi Lu, Zeyu Xia, Yichi Zhang, Songming Liu, Lu Lu, Jun Zhu

CoRR, 2023

Reward Informed Dreamer for Task Generalization in Reinforcement Learning.

Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Songming Liu, Jialian Li, Dong Yan, Jun Zhu

CoRR, 2023

Full-Atom Protein Pocket Design via Iterative Refinement.

Zaixi Zhang, Zepu Lu, Zhongkai Hao, Marinka Zitnik, Qi Liu

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Reuse Bias in Off-Policy Reinforcement Learning.

Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Dong Yan, Jun Zhu

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks.

Jiachen Yao, Chang Su, Zhongkai Hao, Songming Liu, Hang Su, Jun Zhu

Proceedings of the International Conference on Machine Learning, 2023

NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data.

Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu

Proceedings of the International Conference on Machine Learning, 2023

GNOT: A General Neural Operator Transformer for Operator Learning.

Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong, Songming Liu, Ze Cheng, Jian Song, Jun Zhu

Proceedings of the International Conference on Machine Learning, 2023

Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients.

Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Jian Song, Ze Cheng

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Equivariant Energy-Guided SDE for Inverse Molecular Design.

Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Towards Exploring Large Molecular Space: An Efficient Chemical Genetic Algorithm.

Jian-Fu Zhu, Zhongkai Hao, Qi Liu, Yu Yin, Chengqiang Lu, Zhenya Huang, En-Hong Chen

J. Comput. Sci. Technol., 2022

Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications.

Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu

CoRR, 2022

A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs.

Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Ze Cheng

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Cluster Attack: Query-based Adversarial Attacks on Graph with Graph-Dependent Priors.

Zhengyi Wang, Zhongkai Hao, Ziqiao Wang, Hang Su, Jun Zhu

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing.

Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song, Jun Zhu

Proceedings of the International Conference on Machine Learning, 2022

AVT: Au-Assisted Visual Transformer for Facial Expression Recognition.

Rijin Jin, Sirui Zhao, Zhongkai Hao, Yifan Xu, Tong Xu, Enhong Chen

Proceedings of the 2022 IEEE International Conference on Image Processing, 2022

2021

A two-stage 3D CNN based learning method for spontaneous micro-expression recognition.

Sirui Zhao, Hanqing Tao, Yangsong Zhang, Tong Xu, Kun Zhang, Zhongkai Hao, Enhong Chen

Neurocomputing, 2021

Query-based Adversarial Attacks on Graph with Fake Nodes.

Zhengyi Wang, Zhongkai Hao, Hang Su, Jun Zhu

CoRR, 2021

2020

ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction.

Zhongkai Hao, Chengqiang Lu, Zhenya Huang, Hao Wang, Zheyuan Hu, Qi Liu, Enhong Chen, Cheekong Lee

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020