| Generative adversarial imitation learning J Ho, S Ermon Advances in Neural Information Processing Systems, 4565-4573, 2016 | 2373 | 2016 |
| 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 | 1382 | 2016 |
| 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 | 803 | 2021 |
| Generative modeling by estimating gradients of the data distribution Y Song, S Ermon Advances in neural information processing systems 32, 2019 | 787 | 2019 |
| 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 | 755 | 2020 |
| 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 | 705 | 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 | 586* | 2019 |
| A dirt-t approach to unsupervised domain adaptation R Shu, HH Bui, H Narui, S Ermon arXiv preprint arXiv:1802.08735, 2018 | 510 | 2018 |
| Denoising diffusion implicit models J Song, C Meng, S Ermon arXiv preprint arXiv:2010.02502, 2020 | 473 | 2020 |
| 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 | 464 | 2017 |
| Accurate uncertainties for deep learning using calibrated regression V Kuleshov, N Fenner, S Ermon International conference on machine learning, 2796-2804, 2018 | 443 | 2018 |
| 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 | 416 | 2016 |
| 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 | 413 | 2017 |
| Infogail: Interpretable imitation learning from visual demonstrations Y Li, J Song, S Ermon Advances in Neural Information Processing Systems 30, 2017 | 406* | 2017 |
| 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 | 403 | 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 | 387 | 2020 |
| 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 | 377 | 2019 |
| A survey on behavior recognition using WiFi channel state information S Yousefi, H Narui, S Dayal, S Ermon, S Valaee IEEE Communications Magazine 55 (10), 98-104, 2017 | 327 | 2017 |
| Label-free supervision of neural networks with physics and domain knowledge R Stewart, S Ermon Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 319 | 2017 |
| Improved techniques for training score-based generative models Y Song, S Ermon Advances in neural information processing systems 33, 12438-12448, 2020 | 285 | 2020 |
