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Minkai Xu
Minkai Xu
Verified email at cs.stanford.edu - Homepage
Title
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Cited by
Year
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
409*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
3762022
Learning gradient fields for molecular conformation generation
C Shi, S Luo, M Xu, J Tang
International conference on machine learning, 9558-9568, 2021
1842021
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
1502020
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
1142020
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
862021
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
762021
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
712023
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
592023
Energy-based imitation learning
M Liu, T He, M Xu, W Zhang
arXiv preprint arXiv:2004.09395, 2020
472020
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
322024
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
312022
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
202024
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
182024
An all-atom protein generative model
AE Chu, L Cheng, G El Nesr, M Xu, PS Huang
bioRxiv, 2023
142023
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
142020
Madiff: Offline multi-agent learning with diffusion models
Z Zhu, M Liu, L Mao, B Kang, M Xu, Y Yu, S Ermon, W Zhang
arXiv preprint arXiv:2305.17330, 2023
122023
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
112001
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
102020
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
102020
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