| 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 | 526 | 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 | 129 | 2023 |
| Fast training of diffusion models with masked transformers H Zheng, W Nie, A Vahdat, A Anandkumar arXiv preprint arXiv:2306.09305, 2023 | 53 | 2023 |
| Langevin monte carlo for contextual bandits P Xu, H Zheng, EV Mazumdar, K Azizzadenesheli, A Anandkumar International Conference on Machine Learning, 24830-24850, 2022 | 42 | 2022 |
| Implicit competitive regularization in GANs F Schäfer, H Zheng, A Anandkumar arXiv preprint arXiv:1910.05852, 2019 | 36 | 2019 |
| Physics-informed neural operator for learning partial differential equations. arXiv Z Li, H Zheng, N Kovachki, D Jin, H Chen, B Liu, K Azizzadenesheli, ... arXiv preprint arXiv:2111.03794, 2021 | 8 | 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 | 3 | 2024 |
| InverseBench: Benchmarking Plug-and-Play Diffusion Models for Scientific Inverse Problems H Zheng, W Chu, B Zhang, Z Wu, A Wang, B Feng, C Zou, Y Sun, ... The Thirteenth International Conference on Learning Representations, 0 | | |
