| Score-Based Generative Modeling through Stochastic Differential Equations Y Song, J Sohl-Dickstein, DP Kingma, A Kumar, S Ermon, B Poole International Conference on Learning Representations, 2021 | 3862 | 2021 |
| Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 3062* | 2023 |
| Generative modeling by estimating gradients of the data distribution Y Song, S Ermon Advances in Neural Information Processing Systems, 11918-11930, 2019 | 2728 | 2019 |
| SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations C Meng, Y He, Y Song, J Song, J Wu, JY Zhu, S Ermon International Conference on Learning Representations, 2021 | 1112* | 2021 |
| Pixeldefend: Leveraging generative models to understand and defend against adversarial examples Y Song, T Kim, S Nowozin, S Ermon, N Kushman International Conference on Learning Representations, 2018 | 898 | 2018 |
| Diffusion models: A comprehensive survey of methods and applications L Yang, Z Zhang, Y Song, S Hong, R Xu, Y Zhao, W Zhang, B Cui, ... ACM Computing Surveys, 2022 | 881 | 2022 |
| Improved techniques for training score-based generative models Y Song, S Ermon Advances in Neural Information Processing Systems 33, 2020 | 875 | 2020 |
| Maximum Likelihood Training of Score-Based Diffusion Models Y Song, C Durkan, I Murray, S Ermon arXiv preprint arXiv:2101.09258, 2021 | 463 | 2021 |
| Consistency Models Y Song, P Dhariwal, M Chen, I Sutskever arXiv preprint arXiv:2303.01469, 2023 | 424 | 2023 |
| GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation M Xu, L Yu, Y Song, C Shi, S Ermon, J Tang International Conference on Learning Representations, 2021 | 403 | 2021 |
| Sliced score matching: A scalable approach to density and score estimation Y Song, S Garg, J Shi, S Ermon Uncertainty in Artificial Intelligence, 574-584, 2019 | 372 | 2019 |
| Solving Inverse Problems in Medical Imaging with Score-Based Generative Models Y Song, L Shen, L Xing, S Ermon arXiv preprint arXiv:2111.08005, 2021 | 351 | 2021 |
| Efficient graph generation with graph recurrent attention networks R Liao, Y Li, Y Song, S Wang, W Hamilton, DK Duvenaud, R Urtasun, ... Advances in Neural Information Processing Systems, 4255-4265, 2019 | 341 | 2019 |
| Constructing Unrestricted Adversarial Examples with Generative Models Y Song, R Shu, N Kushman, S Ermon Advances in Neural Information Processing Systems, 8322-8333, 2018 | 325 | 2018 |
| 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 | 314 | 2021 |
| How to Train Your Energy-Based Models Y Song, DP Kingma arXiv preprint arXiv:2101.03288, 2021 | 232 | 2021 |
| 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 | 187 | 2020 |
| Training deep neural networks via direct loss minimization Y Song, A Schwing, R Zemel, R Urtasun International Conference on Machine Learning, 2169-2177, 2016 | 118 | 2016 |
| Learning Energy-Based Models by Diffusion Recovery Likelihood R Gao, Y Song, B Poole, YN Wu, DP Kingma International Conference on Learning Representations, 2020 | 114 | 2020 |
| Diversity can be Transferred: Output Diversification for White-and Black-box Attacks Y Tashiro, Y Song, S Ermon Advances in Neural Information Processing Systems 33, 2020 | 100* | 2020 |
Stefano ErmonStanford University在 cs.stanford.edu 的电子邮件经过验证
