| GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation M Xu, L Yu, Y Song, C Shi, S Ermon, J Tang ICLR 2022, 2022 | 590 | 2022 |
| GraphAF: a flow-based autoregressive model for molecular graph generation M Xu*, C Shi*, Z Zhu, W Zhang, M Zhang, J Tang The 8th International Conference on Learning Representations (ICLR 2020), 2020 | 517* | 2020 |
| Learning gradient fields for molecular conformation generation C Shi, S Luo, M Xu, J Tang International conference on machine learning, 9558-9568, 2021 | 243 | 2021 |
| A graph to graphs framework for retrosynthesis prediction C Shi, M Xu, H Guo, M Zhang, J Tang International conference on machine learning, 8818-8827, 2020 | 185 | 2020 |
| Geometric latent diffusion models for 3d molecule generation M Xu, AS Powers, RO Dror, S Ermon, J Leskovec International Conference on Machine Learning, 38592-38610, 2023 | 154 | 2023 |
| Learning Neural Generative Dynamics for Molecular Conformation Generation M Xu, S Luo, Y Bengio, J Peng, J Tang The 9th International Conference on Learning Representations (ICLR 2021), 2020 | 145 | 2020 |
| Artificial intelligence for science in quantum, atomistic, and continuum systems X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, ... arXiv preprint arXiv:2307.08423, 2023 | 134 | 2023 |
| Predicting molecular conformation via dynamic graph score matching S Luo, C Shi, M Xu, J Tang Advances in neural information processing systems 34, 19784-19795, 2021 | 114 | 2021 |
| An end-to-end framework for molecular conformation generation via bilevel programming M Xu, W Wang, S Luo, C Shi, Y Bengio, R Gomez-Bombarelli, J Tang International conference on machine learning, 11537-11547, 2021 | 104 | 2021 |
| Mastering text-to-image diffusion: Recaptioning, planning, and generating with multimodal llms L Yang, Z Yu, C Meng, M Xu, S Ermon, CUI Bin Forty-first International Conference on Machine Learning, 2024 | 96 | 2024 |
| When do graph neural networks help with node classification? investigating the homophily principle on node distinguishability S Luan, C Hua, M Xu, Q Lu, J Zhu, XW Chang, J Fu, J Leskovec, D Precup Advances in Neural Information Processing Systems 36, 28748-28760, 2023 | 80 | 2023 |
| Energy-based imitation learning M Liu, T He, M Xu, W Zhang arXiv preprint arXiv:2004.09395, 2020 | 61 | 2020 |
| An all-atom protein generative model AE Chu, J Kim, L Cheng, G El Nesr, M Xu, RW Shuai, PS Huang Proceedings of the National Academy of Sciences 121 (27), e2311500121, 2024 | 51 | 2024 |
| Equivariant flow matching with hybrid probability transport for 3d molecule generation Y Song, J Gong, M Xu, Z Cao, Y Lan, S Ermon, H Zhou, WY Ma Advances in Neural Information Processing Systems 36, 549-568, 2023 | 48 | 2023 |
| Mudiff: Unified diffusion for complete molecule generation C Hua, S Luan, M Xu, Z Ying, J Fu, S Ermon, D Precup Learning on Graphs Conference, 33: 1-33: 26, 2024 | 47 | 2024 |
| Generative coarse-graining of molecular conformations W Wang, M Xu, C Cai, BK Miller, T Smidt, Y Wang, J Tang, ... arXiv preprint arXiv:2201.12176, 2022 | 43 | 2022 |
| Madiff: Offline multi-agent learning with diffusion models Z Zhu, M Liu, L Mao, B Kang, M Xu, Y Yu, S Ermon, W Zhang Advances in Neural Information Processing Systems 37, 4177-4206, 2024 | 36 | 2024 |
| Buffer of thoughts: Thought-augmented reasoning with large language models L Yang, Z Yu, T Zhang, S Cao, M Xu, W Zhang, JE Gonzalez, B Cui Advances in Neural Information Processing Systems 37, 113519-113544, 2024 | 28 | 2024 |
| The heterophilic graph learning handbook: Benchmarks, models, theoretical analysis, applications and challenges S Luan, C Hua, Q Lu, L Ma, L Wu, X Wang, M Xu, XW Chang, D Precup, ... arXiv preprint arXiv:2407.09618, 2024 | 28 | 2024 |
| Vqgraph: Rethinking graph representation space for bridging gnns and mlps L Yang, Y Tian, M Xu, Z Liu, S Hong, W Qu, W Zhang, B Cui, M Zhang, ... arXiv preprint arXiv:2308.02117, 2023 | 26 | 2023 |
Stefano ErmonStanford UniversityBestätigte E-Mail-Adresse bei cs.stanford.edu