| Generative adversarial imitation learning J Ho, S Ermon Advances in Neural Information Processing Systems, 4565-4573, 2016 | 2895 | 2016 |
| Score-based generative modeling through stochastic differential equations Y Song, J Sohl-Dickstein, DP Kingma, A Kumar, S Ermon, B Poole arXiv preprint arXiv:2011.13456, 2020 | 2017 | 2020 |
| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 1898 | 2021 |
| Denoising diffusion implicit models J Song, C Meng, S Ermon arXiv preprint arXiv:2010.02502, 2020 | 1701 | 2020 |
| Generative modeling by estimating gradients of the data distribution Y Song, S Ermon Advances in neural information processing systems 32, 2019 | 1701 | 2019 |
| Combining satellite imagery and machine learning to predict poverty N Jean, M Burke, M Xie, WM Davis, DB Lobell, S Ermon Science 353 (6301), 790-794, 2016 | 1572 | 2016 |
| Pixeldefend: Leveraging generative models to understand and defend against adversarial examples Y Song, T Kim, S Nowozin, S Ermon, N Kushman arXiv preprint arXiv:1710.10766, 2017 | 807 | 2017 |
| 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 | 674* | 2019 |
| A dirt-t approach to unsupervised domain adaptation R Shu, HH Bui, H Narui, S Ermon arXiv preprint arXiv:1802.08735, 2018 | 595 | 2018 |
| Improved techniques for training score-based generative models Y Song, S Ermon Advances in neural information processing systems 33, 12438-12448, 2020 | 581 | 2020 |
| Mopo: Model-based offline policy optimization T Yu, G Thomas, L Yu, S Ermon, JY Zou, S Levine, C Finn, T Ma Advances in Neural Information Processing Systems 33, 14129-14142, 2020 | 565 | 2020 |
| Closed-loop optimization of fast-charging protocols for batteries with machine learning PM Attia, A Grover, N Jin, KA Severson, TM Markov, YH Liao, MH Chen, ... Nature 578 (7795), 397-402, 2020 | 555 | 2020 |
| Accurate uncertainties for deep learning using calibrated regression V Kuleshov, N Fenner, S Ermon International conference on machine learning, 2796-2804, 2018 | 553 | 2018 |
| 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 | 551 | 2021 |
| Coupling between oxygen redox and cation migration explains unusual electrochemistry in lithium-rich layered oxides WE Gent, K Lim, Y Liang, Q Li, T Barnes, SJ Ahn, KH Stone, M McIntire, ... Nature communications 8 (1), 2091, 2017 | 516 | 2017 |
| Deep gaussian process for crop yield prediction based on remote sensing data J You, X Li, M Low, D Lobell, S Ermon Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017 | 501 | 2017 |
| Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning CS Ho, N Jean, CA Hogan, L Blackmon, SS Jeffrey, M Holodniy, N Banaei, ... Nature communications 10 (1), 4927, 2019 | 485 | 2019 |
| Transfer learning from deep features for remote sensing and poverty mapping M Xie, N Jean, M Burke, D Lobell, S Ermon Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 466 | 2016 |
| Infogail: Interpretable imitation learning from visual demonstrations Y Li, J Song, S Ermon Advances in neural information processing systems 30, 2017 | 448 | 2017 |
| 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 | 437 | 2022 |
