| Generative adversarial imitation learning J Ho, S Ermon Advances in Neural Information Processing Systems, 4565-4573, 2016 | 1079 | 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 | 770 | 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 | 362 | 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 | 290* | 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 | 240 | 2016 |
| A dirt-t approach to unsupervised domain adaptation R Shu, HH Bui, H Narui, S Ermon arXiv preprint arXiv:1802.08735, 2018 | 226 | 2018 |
| 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), 1-12, 2017 | 223 | 2017 |
| Infogail: Interpretable imitation learning from visual demonstrations Y Li, J Song, S Ermon arXiv preprint arXiv:1703.08840, 2017 | 215* | 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 | 186 | 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 | 174 | 2017 |
| Accurate uncertainties for deep learning using calibrated regression V Kuleshov, N Fenner, S Ermon International Conference on Machine Learning, 2796-2804, 2018 | 138 | 2018 |
| 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 | 130 | 2017 |
| Graphite: Iterative generative modeling of graphs A Grover, A Zweig, S Ermon International conference on machine learning, 2434-2444, 2019 | 116 | 2019 |
| End-to-end learning of motion representation for video understanding L Fan, W Huang, C Gan, S Ermon, B Gong, J Huang Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 114 | 2018 |
| Constructing unrestricted adversarial examples with generative models Y Song, R Shu, N Kushman, S Ermon arXiv preprint arXiv:1805.07894, 2018 | 113* | 2018 |
| Taming the curse of dimensionality: Discrete integration by hashing and optimization S Ermon, C Gomes, A Sabharwal, B Selman International Conference on Machine Learning, 334-342, 2013 | 113 | 2013 |
| Flow-gan: Combining maximum likelihood and adversarial learning in generative models A Grover, M Dhar, S Ermon Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 96* | 2018 |
| Generative modeling by estimating gradients of the data distribution Y Song, S Ermon arXiv preprint arXiv:1907.05600, 2019 | 87 | 2019 |
| Towards deeper understanding of variational autoencoding models S Zhao, J Song, S Ermon arXiv preprint arXiv:1702.08658, 2017 | 81 | 2017 |
| Learning hierarchical features from deep generative models S Zhao, J Song, S Ermon International Conference on Machine Learning, 4091-4099, 2017 | 80* | 2017 |
