Stefano Ermon
Geciteerd door
Geciteerd door
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
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
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
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
A dirt-t approach to unsupervised domain adaptation
R Shu, HH Bui, H Narui, S Ermon
arXiv preprint arXiv:1802.08735, 2018
Infogail: Interpretable imitation learning from visual demonstrations
Y Li, J Song, S Ermon
arXiv preprint arXiv:1703.08840, 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
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
Accurate uncertainties for deep learning using calibrated regression
V Kuleshov, N Fenner, S Ermon
International Conference on Machine Learning, 2796-2804, 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
Graphite: Iterative generative modeling of graphs
A Grover, A Zweig, S Ermon
International conference on machine learning, 2434-2444, 2019
Generative modeling by estimating gradients of the data distribution
Y Song, S Ermon
arXiv preprint arXiv:1907.05600, 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
Constructing unrestricted adversarial examples with generative models
Y Song, R Shu, N Kushman, S Ermon
arXiv preprint arXiv:1805.07894, 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
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
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
High‐voltage charging‐induced strain, heterogeneity, and micro‐cracks in secondary particles of a nickel‐rich layered cathode material
Y Mao, X Wang, S Xia, K Zhang, C Wei, S Bak, Z Shadike, X Liu, Y Yang, ...
Advanced Functional Materials 29 (18), 1900247, 2019
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