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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
80622017
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
35322018
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
22092018
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
18892018
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 vision …, 2021
13342021
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
12522019
Adversarial patch
TB Brown, D Mané, A Roy, M Abadi, J Gilmer
Advances in Neural Information Processing Systems (Workshop Track), 2017
9542017
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
7982023
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
6702017
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
6652017
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
4832019
Deep information propagation
SS Schoenholz, J Gilmer, S Ganguli, J Sohl-Dickstein
International Conference on Learning Representations 2017, 2016
4022016
Adversarial spheres
J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ...
arXiv preprint arXiv:1801.02774, 2018
3852018
Scaling vision transformers to 22 billion parameters
M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ...
International Conference on Machine Learning, 7480-7512, 2023
3162023
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
2472018
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
2082019
Mnist-c: A robustness benchmark for computer vision
N Mu, J Gilmer
arXiv preprint arXiv:1906.02337, 2019
1892019
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
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
1782019
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
1772019
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