| 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 | 199* | 2020 |
| 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 | 83 | 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 | 67 | 2022 |
| Learning gradient fields for molecular conformation generation C Shi, S Luo, M Xu, J Tang International Conference on Machine Learning, 9558-9568, 2021 | 67 | 2021 |
| 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 | 48 | 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 | 31 | 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 | 31 | 2021 |
| Energy-based imitation learning M Liu, T He, M Xu, W Zhang arXiv preprint arXiv:2004.09395, 2020 | 22 | 2020 |
| 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 | 9 | 2022 |
| 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 | 8 | 2020 |
| 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 | 7 | 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 | 7 | 2001 |
| 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 | 4 | 2020 |
| Reciprocal Supervised Learning Improves Neural Machine Translation M Xu, M Wang, Z Lin, H Zhou, W Zhang, L Li arXiv preprint arXiv:2012.02975, 2020 | | 2020 |
| A GRAPH TO GRAPHS FRAMEWORK FOR RETROSYNTHESIS PREDICTION CHANCE C Shi, M Xu, H Guo, M Zhang, J Tang | | |
Jian Tang (唐建）Associate Professor, Mila-Quebec AI Institute, HEC Montréal, Canada CIFAR AI ChairBestätigte E-Mail-Adresse bei hec.ca
