Stefano Ermon
Cited by
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Generative adversarial imitation learning
J Ho, S Ermon
Advances in Neural Information Processing Systems, 4565-4573, 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
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
A dirt-t approach to unsupervised domain adaptation
R Shu, HH Bui, H Narui, S Ermon
arXiv preprint arXiv:1802.08735, 2018
Transfer learning from deep features for remote sensing and poverty mapping
M Xie, N Jean, M Burke, D Lobell, S Ermon
arXiv preprint arXiv:1510.00098, 2015
Infovae: Information maximizing variational autoencoders
S Zhao, J Song, S Ermon
arXiv preprint arXiv:1706.02262, 2017
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
Infogail: Interpretable imitation learning from visual demonstrations
Y Li, J Song, S Ermon
Advances in Neural Information Processing Systems, 3812-3822, 2017
Label-free supervision of neural networks with physics and domain knowledge
R Stewart, S Ermon
Thirty-First AAAI Conference on Artificial Intelligence, 2017
Deep gaussian process for crop yield prediction based on remote sensing data
J You, X Li, M Low, D Lobell, S Ermon
Thirty-First AAAI conference on artificial intelligence, 2017
Accurate uncertainties for deep learning using calibrated regression
V Kuleshov, N Fenner, S Ermon
arXiv preprint arXiv:1807.00263, 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
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
Constructing unrestricted adversarial examples with generative models
Y Song, R Shu, N Kushman, S Ermon
Advances in Neural Information Processing Systems, 8312-8323, 2018
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
Graphite: Iterative generative modeling of graphs
A Grover, A Zweig, S Ermon
International conference on machine learning, 2434-2444, 2019
Flow-gan: Combining maximum likelihood and adversarial learning in generative models
A Grover, M Dhar, S Ermon
arXiv preprint arXiv:1705.08868, 2017
Towards deeper understanding of variational autoencoding models
S Zhao, J Song, S Ermon
arXiv preprint arXiv:1702.08658, 2017
Model-free imitation learning with policy optimization
J Ho, J Gupta, S Ermon
International Conference on Machine Learning, 2760-2769, 2016
Embed and project: Discrete sampling with universal hashing
S Ermon, CP Gomes, A Sabharwal, B Selman
Advances in Neural Information Processing Systems, 2085-2093, 2013
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