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Justin Gilmer
Justin Gilmer
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Cited by
Year
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
40152017
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
20812018
Sanity checks for saliency maps
J Adebayo, J Gilmer, M Muelly, I Goodfellow, M Hardt, B Kim
Advances in neural information processing systems 31, 2018
11532018
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
9112018
Adversarial patch
TB Brown, D ManÚ, A Roy, M Abadi, J Gilmer
Advances in Neural Information Processing Systems (Workshop Track), 2017
5582017
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
4892019
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
4822017
Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
M Raghu, J Gilmer, J Yosinski, J Sohl-Dickstein
Advances in neural information processing systems 30, 2017
4092017
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
3322021
Adversarial spheres
J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ...
arXiv preprint arXiv:1801.02774, 2018
2882018
Deep information propagation
SS Schoenholz, J Gilmer, S Ganguli, J Sohl-Dickstein
International Conference on Learning Representations 2017, 2016
2722016
A fourier perspective on model robustness in computer vision
D Yin, R Gontijo Lopes, J Shlens, ED Cubuk, J Gilmer
Advances in Neural Information Processing Systems 32, 2019
2512019
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
1852018
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
1342019
Proceedings of the 34th International Conference on Machine Learning
J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl
PMLR 70, 1263-1272, 2017
1342017
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
1222019
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
1112019
Mnist-c: A robustness benchmark for computer vision
N Mu, J Gilmer
arXiv preprint arXiv:1906.02337, 2019
932019
Relational inductive biases, deep learning, and graph networks. arXiv 2018
PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ...
arXiv preprint arXiv:1806.01261, 2018
712018
Augmix: A simple method to improve robustness and uncertainty under data shift
D Hendrycks, N Mu, ED Cubuk, B Zoph, J Gilmer, B Lakshminarayanan
International conference on learning representations 1 (4), 6, 2020
552020
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