| Denoising diffusion implicit models J Song, C Meng, S Ermon International Conference on Learning Representations (ICLR) 2021, 2020 | 7110 | 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 | 1721 | 2021 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 1393 | 2022 |
| 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 | 874* | 2019 |
| Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA SP Singh, S Markovitch AAAI press, 2017 | 834* | 2017 |
| Denoising diffusion restoration models B Kawar, M Elad, S Ermon, J Song Advances in Neural Information Processing Systems 35, 23593-23606, 2022 | 821 | 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 | 753 | 2022 |
| 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 | 552 | 2021 |
| Infogail: Interpretable imitation learning from visual demonstrations Y Li, J Song, S Ermon Advances in neural information processing systems 30, 2017 | 506 | 2017 |
| Multi-agent generative adversarial imitation learning J Song, H Ren, D Sadigh, S Ermon Neural Information Processing Systems (NeurIPS) 2018, 2018 | 291 | 2018 |
| 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 | 271 | 2020 |
| Pseudoinverse-guided diffusion models for inverse problems J Song, A Vahdat, M Mardani, J Kautz International Conference on Learning Representations, 2023 | 261 | 2023 |
| 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 | 241 | 2023 |
| Understanding the limitations of variational mutual information estimators J Song, S Ermon International Conference on Learning Representations (ICLR) 2020, 2019 | 239 | 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 | 216 | 2019 |
| Towards deeper understanding of variational autoencoding models S Zhao, J Song, S Ermon arXiv preprint arXiv:1702.08658, 2017 | 213 | 2017 |
| Dual diffusion implicit bridges for image-to-image translation X Su, J Song, C Meng, S Ermon arXiv preprint arXiv:2203.08382, 2022 | 210 | 2022 |
| Iq-learn: Inverse soft-q learning for imitation D Garg, S Chakraborty, C Cundy, J Song, S Ermon Advances in Neural Information Processing Systems 34, 4028-4039, 2021 | 203 | 2021 |
| Learning hierarchical features from deep generative models S Zhao, J Song, S Ermon International Conference on Machine Learning, 4091-4099, 2017 | 177* | 2017 |
| 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 | 171 | 2020 |
Stefano ErmonStanford UniversityBestätigte E-Mail-Adresse bei cs.stanford.edu
