| Denoising diffusion implicit models J Song, C Meng, S Ermon International Conference on Learning Representations (ICLR) 2021, 2020 | 4025 | 2020 |
| Sdedit: Guided image synthesis and editing with stochastic differential equations C Meng, Y He, Y Song, J Song, J Wu, JY Zhu, S Ermon arXiv preprint arXiv:2108.01073, 2021 | 1145 | 2021 |
| Infovae: Balancing learning and inference in variational autoencoders S Zhao, J Song, S Ermon Proceedings of the aaai conference on artificial intelligence 33 (01), 5885-5892, 2019 | 775* | 2019 |
| Denoising diffusion restoration models B Kawar, M Elad, S Ermon, J Song Advances in Neural Information Processing Systems 35, 23593-23606, 2022 | 529 | 2022 |
| ediff-i: Text-to-image diffusion models with an ensemble of expert denoisers Y Balaji, S Nah, X Huang, A Vahdat, J Song, Q Zhang, K Kreis, M Aittala, ... arXiv preprint arXiv:2211.01324, 2022 | 519 | 2022 |
| Proceedings of the 26th International Joint Conference on Artificial Intelligence C Sierra AAAI Press, 2017 | 498 | 2017 |
| Infogail: Interpretable imitation learning from visual demonstrations Y Li, J Song, S Ermon Advances in neural information processing systems 30, 2017 | 459* | 2017 |
| Csdi: Conditional score-based diffusion models for probabilistic time series imputation Y Tashiro, J Song, Y Song, S Ermon Advances in Neural Information Processing Systems 34, 24804-24816, 2021 | 326 | 2021 |
| Multi-agent generative adversarial imitation learning J Song, H Ren, D Sadigh, S Ermon Neural Information Processing Systems (NeurIPS) 2018, 2018 | 254 | 2018 |
| Understanding the limitations of variational mutual information estimators J Song, S Ermon International Conference on Learning Representations (ICLR) 2020, 2019 | 196 | 2019 |
| Learning controllable fair representations J Song, P Kalluri, A Grover, S Zhao, S Ermon The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 195 | 2019 |
| Towards deeper understanding of variational autoencoding models S Zhao, J Song, S Ermon arXiv preprint arXiv:1702.08658, 2017 | 190 | 2017 |
| Permutation invariant graph generation via score-based generative modeling C Niu, Y Song, J Song, S Zhao, A Grover, S Ermon International Conference on Artificial Intelligence and Statistics, 4474-4484, 2020 | 189 | 2020 |
| Learning hierarchical features from deep generative models S Zhao, J Song, S Ermon International Conference on Machine Learning, 4091-4099, 2017 | 167* | 2017 |
| Dual diffusion implicit bridges for image-to-image translation X Su, J Song, C Meng, S Ermon arXiv preprint arXiv:2203.08382, 2022 | 145 | 2022 |
| Physdiff: Physics-guided human motion diffusion model Y Yuan, J Song, U Iqbal, A Vahdat, J Kautz Proceedings of the IEEE/CVF international conference on computer vision …, 2023 | 141 | 2023 |
| A theory of usable information under computational constraints Y Xu, S Zhao, J Song, R Stewart, S Ermon nternational Conference on Learning Representations (ICLR) 2020, 2020 | 141 | 2020 |
| Bias and generalization in deep generative models: An empirical study S Zhao, H Ren, A Yuan, J Song, N Goodman, S Ermon Neural Information Processing Systems (NeurIPS) 2018, 2018 | 139 | 2018 |
| Multi-agent adversarial inverse reinforcement learning L Yu, J Song, S Ermon International Conference on Machine Learning, 7194-7201, 2019 | 136 | 2019 |
| Pseudoinverse-guided diffusion models for inverse problems J Song, A Vahdat, M Mardani, J Kautz International Conference on Learning Representations, 2023 | 135 | 2023 |
Stefano ErmonStanford University在 cs.stanford.edu 的电子邮件经过验证
