| Denoising diffusion implicit models J Song, C Meng, S Ermon International Conference on Learning Representations (ICLR) 2021, 2020 | 3098 | 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 | 919 | 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 | 735* | 2019 |
| Infogail: Interpretable imitation learning from visual demonstrations Y Li, J Song, S Ermon Advances in neural information processing systems 30, 2017 | 443 | 2017 |
| Denoising diffusion restoration models B Kawar, M Elad, S Ermon, J Song Advances in Neural Information Processing Systems 35, 23593-23606, 2022 | 422 | 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 | 419 | 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 | 258 | 2021 |
| Multi-agent generative adversarial imitation learning J Song, H Ren, D Sadigh, S Ermon Neural Information Processing Systems (NeurIPS) 2018, 2018 | 240 | 2018 |
| Towards deeper understanding of variational autoencoding models S Zhao, J Song, S Ermon arXiv preprint arXiv:1702.08658, 2017 | 188 | 2017 |
| Understanding the limitations of variational mutual information estimators J Song, S Ermon International Conference on Learning Representations (ICLR) 2020, 2019 | 184 | 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 | 184 | 2019 |
| Learning hierarchical features from deep generative models S Zhao, J Song, S Ermon International Conference on Machine Learning, 4091-4099, 2017 | 162* | 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 | 153 | 2020 |
| Bias correction of learned generative models using likelihood-free importance weighting A Grover, J Song, A Agarwal, K Tran, A Kapoor, E Horvitz, S Ermon Neural Information Processing Systems (NeurIPS) 2019, 2019 | 129 | 2019 |
| 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 | 128 | 2020 |
| A-nice-mc: Adversarial training for mcmc J Song, S Zhao, S Ermon Neural Information Processing Systems (NeurIPS) 2017, 2017 | 128 | 2017 |
| 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 | 126 | 2018 |
| Multi-agent adversarial inverse reinforcement learning L Yu, J Song, S Ermon International Conference on Machine Learning, 7194-7201, 2019 | 124 | 2019 |
| Dual diffusion implicit bridges for image-to-image translation X Su, J Song, C Meng, S Ermon arXiv preprint arXiv:2203.08382, 2022 | 116 | 2022 |
| Max-margin nonparametric latent feature models for link prediction J Zhu, J Song, B Chen arXiv preprint arXiv:1602.07428, 2016 | 110 | 2016 |
Stefano ErmonStanford UniversityPotvrđena adresa e-pošte na cs.stanford.edu
