| Physics-informed neural operator for learning partial differential equations Z Li, H Zheng, N Kovachki, D Jin, H Chen, B Liu, K Azizzadenesheli, ... ACM/JMS Journal of Data Science 1 (3), 1-27, 2024 | 432 | 2024 |
| Fast sampling of diffusion models via operator learning H Zheng, W Nie, A Vahdat, K Azizzadenesheli, A Anandkumar International conference on machine learning, 42390-42402, 2023 | 110 | 2023 |
| Fast training of diffusion models with masked transformers H Zheng, W Nie, A Vahdat, A Anandkumar arXiv preprint arXiv:2306.09305, 2023 | 42 | 2023 |
| Implicit competitive regularization in GANs F Schäfer, H Zheng, A Anandkumar arXiv preprint arXiv:1910.05852, 2019 | 35 | 2019 |
| Langevin monte carlo for contextual bandits P Xu, H Zheng, EV Mazumdar, K Azizzadenesheli, A Anandkumar International Conference on Machine Learning, 24830-24850, 2022 | 34 | 2022 |
| Physics-informed neural operator for learning partial differential equations. arXiv 2021 Z Li, H Zheng, N Kovachki, D Jin, H Chen, B Liu, K Azizzadenesheli, ... arXiv preprint arXiv:2111.03794, 0 | 7 | |
| Anima Anandkumar, Physics-informed neural operator for learning partial differential equations Z Li, H Zheng, N Kovachki, D Jin, H Chen, B Liu, K Azizzadenesheli arXiv preprint arXiv:2111.03794, 2021 | 5 | 2021 |
| Ensemble Kalman Diffusion Guidance: A Derivative-free Method for Inverse Problems H Zheng, W Chu, A Wang, N Kovachki, R Baptista, Y Yue arXiv preprint arXiv:2409.20175, 2024 | | 2024 |