Stefano Ermon
Cited by
Cited by
Denoising diffusion implicit models
J Song, C Meng, S Ermon
arXiv preprint arXiv:2010.02502, 2020
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
Generative adversarial imitation learning
J Ho, S Ermon
Advances in Neural Information Processing Systems, 4565-4573, 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
Generative modeling by estimating gradients of the data distribution
Y Song, S Ermon
Advances in neural information processing systems 32, 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
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
Flashattention: Fast and memory-efficient exact attention with io-awareness
T Dao, D Fu, S Ermon, A Rudra, C Ré
Advances in Neural Information Processing Systems 35, 16344-16359, 2022
Direct preference optimization: Your language model is secretly a reward model
R Rafailov, A Sharma, E Mitchell, CD Manning, S Ermon, C Finn
Advances in Neural Information Processing Systems 36, 2024
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
Improved techniques for training score-based generative models
Y Song, S Ermon
Advances in neural information processing systems 33, 12438-12448, 2020
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
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
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
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
A dirt-t approach to unsupervised domain adaptation
R Shu, HH Bui, H Narui, S Ermon
arXiv preprint arXiv:1802.08735, 2018
Accurate uncertainties for deep learning using calibrated regression
V Kuleshov, N Fenner, S Ermon
International conference on machine learning, 2796-2804, 2018
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
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), 1-8, 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), 2091, 2017
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