Justin Gilmer
Justin Gilmer
Verified email at google.com
Cited by
Cited by
Neural message passing for quantum chemistry
J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl
International Conference on Machine Learning 2017, 1263-1272, 2017
Relational inductive biases, deep learning, and graph networks
PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ...
arXiv preprint arXiv:1806.01261, 2018
Sanity checks for saliency maps
J Adebayo, J Gilmer, M Muelly, I Goodfellow, M Hardt, B Kim
arXiv preprint arXiv:1810.03292, 2018
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
B Kim, M Wattenberg, J Gilmer, C Cai, J Wexler, F Viegas
International conference on machine learning, 2668-2677, 2018
Adversarial patch
TB Brown, D ManÚ, A Roy, M Abadi, J Gilmer
Advances in Neural Information Processing Systems (Workshop Track), 2017
Prediction errors of molecular machine learning models lower than hybrid DFT error
FA Faber, L Hutchison, B Huang, J Gilmer, SS Schoenholz, GE Dahl, ...
Journal of chemical theory and computation 13 (11), 5255-5264, 2017
Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
M Raghu, J Gilmer, J Yosinski, J Sohl-Dickstein
arXiv preprint arXiv:1706.05806, 2017
Augmix: A simple data processing method to improve robustness and uncertainty
D Hendrycks, N Mu, ED Cubuk, B Zoph, J Gilmer, B Lakshminarayanan
arXiv preprint arXiv:1912.02781, 2019
Adversarial spheres
J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ...
arXiv preprint arXiv:1801.02774, 2018
Deep information propagation
SS Schoenholz, J Gilmer, S Ganguli, J Sohl-Dickstein
International Conference on Learning Representations 2017, 2016
A fourier perspective on model robustness in computer vision
D Yin, RG Lopes, J Shlens, ED Cubuk, J Gilmer
arXiv preprint arXiv:1906.08988, 2019
Motivating the rules of the game for adversarial example research
J Gilmer, RP Adams, I Goodfellow, D Andersen, GE Dahl
arXiv preprint arXiv:1807.06732, 2018
The many faces of robustness: A critical analysis of out-of-distribution generalization
D Hendrycks, S Basart, N Mu, S Kadavath, F Wang, E Dorundo, R Desai, ...
Proceedings of the IEEE/CVF International Conference on Computer Visioná…, 2021
Adversarial examples are a natural consequence of test error in noise
N Ford, J Gilmer, N Carlini, D Cubuk
arXiv preprint arXiv:1901.10513, 2019
Improving robustness without sacrificing accuracy with patch gaussian augmentation
RG Lopes, D Yin, B Poole, J Gilmer, ED Cubuk
arXiv preprint arXiv:1906.02611, 2019
Proceedings of the 34th International Conference on Machine Learning
J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl
PMLR 70, 1263-1272, 2017
Adversarial examples are a natural consequence of test error in noise
J Gilmer, N Ford, N Carlini, E Cubuk
International Conference on Machine Learning, 2280-2289, 2019
Mnist-c: A robustness benchmark for computer vision
N Mu, J Gilmer
arXiv preprint arXiv:1906.02337, 2019
Input switched affine networks: An RNN architecture designed for interpretability
JN Foerster, J Gilmer, J Chorowski, J Sohl-Dickstein, D Sussillo
International Conference on Machine Learning 2017, 1136-1145, 2016
Composition limits and separating examples for some Boolean function complexity measures
J Gilmer, M Saks, S Srinivasan
Combinatorica 36 (3), 265-311, 2016
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