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 | 395* | 2020 |
GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation M Xu, L Yu, Y Song, C Shi, S Ermon, J Tang arXiv preprint arXiv:2203.02923, 2022 | 355 | 2022 |
Learning gradient fields for molecular conformation generation C Shi, S Luo, M Xu, J Tang International conference on machine learning, 9558-9568, 2021 | 175 | 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 | 145 | 2020 |
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 | 111 | 2020 |
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 | 82 | 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 | 75 | 2021 |
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 | 63 | 2023 |
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 | 51 | 2023 |
Energy-based imitation learning M Liu, T He, M Xu, W Zhang arXiv preprint arXiv:2004.09395, 2020 | 45 | 2020 |
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, 2024 | 31 | 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 | 30 | 2022 |
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 | 17 | 2024 |
Mastering text-to-image diffusion: Recaptioning, planning, and generating with multimodal llms L Yang, Z Yu, C Meng, M Xu, S Ermon, B Cui arXiv preprint arXiv:2401.11708, 2024 | 13 | 2024 |
An all-atom protein generative model AE Chu, L Cheng, G El Nesr, M Xu, PS Huang bioRxiv, 2023 | 13 | 2023 |
Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip Y Song, M Xu, L Yu, H Zhou, S Shao, Y Yu The 34th AAAI Conference on Artificial Intelligence (AAAI 2020), 2020 | 13 | 2020 |
GraphAF: a flow-based autoregressive model for molecular graph generation (2020) C Shi, M Xu, Z Zhu, W Zhang, M Zhang, J Tang arXiv preprint arXiv:2001.09382, 2001 | 11 | 2001 |
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator Y Song*, Q Ye*, M Xu*, TY Liu arXiv preprint arXiv:2004.01704, 2020 | 10 | 2020 |
Coarse-to-fine: a hierarchical diffusion model for molecule generation in 3d B Qiang, Y Song, M Xu, J Gong, B Gao, H Zhou, WY Ma, Y Lan International Conference on Machine Learning, 28277-28299, 2023 | 9 | 2023 |
Towards Generalized Implementation of Wasserstein Distance in GANs M Xu, Z Zhou, G Lu, J Tang, W Zhang, Y Yu The 35th AAAI Conference on Artificial Intelligence (AAAI 2021), 2020 | 9* | 2020 |