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ORCIDZhongkai Hao
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2020 – today
- 2024
[c17]Hong Wang, Zhongkai Hao, Jie Wang, Zijie Geng, Zhen Wang, Bin Li, Feng Wu:
Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling. ICLR 2024- [c16]Ze Cheng, Zhongkai Hao, Xiaoqiang Wang, Jianing Huang, Youjia Wu, Xudan Liu, Yiru Zhao, Songming Liu, Hang Su:
Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations. ICML 2024 - [c15]Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu:
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training. ICML 2024 - [c14]Pengwei Liu, Zhongkai Hao, Xingyu Ren, Hangjie Yuan, Jiayang Ren, Dong Ni:
PAPM: A Physics-aware Proxy Model for Process Systems. ICML 2024 - [c13]Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng, Hang Su:
Improved Operator Learning by Orthogonal Attention. ICML 2024 - [i21]Hong Wang, Zhongkai Hao, Jie Wang, Zijie Geng, Zhen Wang, Bin Li, Feng Wu:
Accelerating Data Generation for Neural Operators via Krylov Subspace Recycling. CoRR abs/2401.09516 (2024) - [i20]Songming Liu, Chang Su, Jiachen Yao, Zhongkai Hao, Hang Su, Youjia Wu, Jun Zhu:
Preconditioning for Physics-Informed Neural Networks. CoRR abs/2402.00531 (2024) - [i19]Huanran Chen, Yinpeng Dong, Shitong Shao, Zhongkai Hao, Xiao Yang, Hang Su, Jun Zhu:
Your Diffusion Model is Secretly a Certifiably Robust Classifier. CoRR abs/2402.02316 (2024) - [i18]Zhongkai Hao, Chang Su, Songming Liu, Julius Berner, Chengyang Ying, Hang Su, Anima Anandkumar, Jian Song, Jun Zhu:
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training. CoRR abs/2403.03542 (2024) - [i17]Chengyang Ying, Zhongkai Hao, Xinning Zhou, Xuezhou Xu, Hang Su, Xingxing Zhang, Jun Zhu:
PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement Learning. CoRR abs/2405.14073 (2024) - [i16]Ze Cheng, Zhongkai Hao, Xiaoqiang Wang, Jianing Huang, Youjia Wu, Xudan Liu, Yiru Zhao, Songming Liu, Hang Su:
Reference Neural Operators: Learning the Smooth Dependence of Solutions of PDEs on Geometric Deformations. CoRR abs/2405.17509 (2024) - [i15]Pengwei Liu, Zhongkai Hao, Xingyu Ren, Hangjie Yuan, Jiayang Ren, Dong Ni:
PAPM: A Physics-aware Proxy Model for Process Systems. CoRR abs/2407.05232 (2024) - 2023
- [c12]Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu:
Equivariant Energy-Guided SDE for Inverse Molecular Design. ICLR 2023 - [c11]Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Jian Song, Ze Cheng:
Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients. ICLR 2023 - [c10]Zhongkai Hao, Zhengyi Wang, Hang Su, Chengyang Ying, Yinpeng Dong, Songming Liu, Ze Cheng, Jian Song, Jun Zhu:
GNOT: A General Neural Operator Transformer for Operator Learning. ICML 2023: 12556-12569 - [c9]Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu:
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data. ICML 2023: 21658-21671 - [c8]Jiachen Yao, Chang Su, Zhongkai Hao, Songming Liu, Hang Su, Jun Zhu:
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks. ICML 2023: 39702-39721 - [c7]Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Dong Yan, Jun Zhu:
On the Reuse Bias in Off-Policy Reinforcement Learning. IJCAI 2023: 4513-4521 - [c6]Zaixi Zhang, Zepu Lu, Zhongkai Hao, Marinka Zitnik, Qi Liu:
Full-Atom Protein Pocket Design via Iterative Refinement. NeurIPS 2023 - [i14]Zhongkai Hao, Chengyang Ying, Zhengyi Wang, Hang Su, Yinpeng Dong, Songming Liu, Ze Cheng, Jun Zhu, Jian Song:
GNOT: A General Neural Operator Transformer for Operator Learning. CoRR abs/2302.14376 (2023) - [i13]Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Songming Liu, Jialian Li, Dong Yan, Jun Zhu:
Reward Informed Dreamer for Task Generalization in Reinforcement Learning. CoRR abs/2303.05092 (2023) - [i12]Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu:
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data. CoRR abs/2305.18694 (2023) - [i11]Jiachen Yao, Chang Su, Zhongkai Hao, Songming Liu, Hang Su, Jun Zhu:
MultiAdam: Parameter-wise Scale-invariant Optimizer for Multiscale Training of Physics-informed Neural Networks. CoRR abs/2306.02816 (2023) - [i10]Zhongkai Hao, Jiachen Yao, Chang Su, Hang Su, Ziao Wang, Fanzhi Lu, Zeyu Xia, Yichi Zhang, Songming Liu, Lu Lu, Jun Zhu:
PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs. CoRR abs/2306.08827 (2023) - [i9]Zipeng Xiao, Zhongkai Hao, Bokai Lin, Zhijie Deng, Hang Su:
Improved Operator Learning by Orthogonal Attention. CoRR abs/2310.12487 (2023) - 2022
- [j2]Jian-Fu Zhu, Zhongkai Hao, Qi Liu, Yu Yin, Chengqiang Lu, Zhenya Huang, En-Hong Chen:
Towards Exploring Large Molecular Space: An Efficient Chemical Genetic Algorithm. J. Comput. Sci. Technol. 37(6): 1464-1477 (2022) - [c5]Rijin Jin, Sirui Zhao, Zhongkai Hao, Yifan Xu, Tong Xu, Enhong Chen:
AVT: Au-Assisted Visual Transformer for Facial Expression Recognition. ICIP 2022: 2661-2665 - [c4]Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jian Song, Jun Zhu:
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing. ICML 2022: 8465-8483 - [c3]Zhengyi Wang, Zhongkai Hao, Ziqiao Wang, Hang Su, Jun Zhu:
Cluster Attack: Query-based Adversarial Attacks on Graph with Graph-Dependent Priors. IJCAI 2022: 768-775 - [c2]Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Ze Cheng:
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs. NeurIPS 2022 - [i8]Zhongkai Hao, Chengyang Ying, Yinpeng Dong, Hang Su, Jun Zhu, Jian Song:
GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing. CoRR abs/2206.04310 (2022) - [i7]Chengyang Ying, Zhongkai Hao, Xinning Zhou, Hang Su, Dong Yan, Jun Zhu:
On the Reuse Bias in Off-Policy Reinforcement Learning. CoRR abs/2209.07074 (2022) - [i6]Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Jian Song, Ze Cheng:
Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients. CoRR abs/2209.07075 (2022) - [i5]Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li, Chongxuan Li, Jun Zhu:
Equivariant Energy-Guided SDE for Inverse Molecular Design. CoRR abs/2209.15408 (2022) - [i4]Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Jun Zhu, Ze Cheng:
A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs. CoRR abs/2210.03526 (2022) - [i3]Zhongkai Hao, Songming Liu, Yichi Zhang, Chengyang Ying, Yao Feng, Hang Su, Jun Zhu:
Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications. CoRR abs/2211.08064 (2022) - 2021
- [j1]Sirui Zhao, Hanqing Tao, Yangsong Zhang, Tong Xu, Kun Zhang, Zhongkai Hao, Enhong Chen
:
A two-stage 3D CNN based learning method for spontaneous micro-expression recognition. Neurocomputing 448: 276-289 (2021) - [i2]Zhengyi Wang, Zhongkai Hao, Hang Su, Jun Zhu:
Query-based Adversarial Attacks on Graph with Fake Nodes. CoRR abs/2109.13069 (2021) - 2020
- [c1]Zhongkai Hao, Chengqiang Lu, Zhenya Huang, Hao Wang, Zheyuan Hu, Qi Liu, Enhong Chen, Cheekong Lee:
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction. KDD 2020: 731-752 - [i1]Zhongkai Hao, Chengqiang Lu, Zheyuan Hu, Hao Wang, Zhenya Huang, Qi Liu, Enhong Chen, Cheekong Lee:
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction. CoRR abs/2007.03196 (2020)
Coauthor Index
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