Justin Gilmer
Justin Gilmer
Verified email at google.com
Title
Cited by
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
9072017
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
4892018
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
235*2017
Adversarial patch
TB Brown, D Mané, A Roy, M Abadi, J Gilmer
Advances in Neural Information Processing Systems (Workshop Track), 2017
222*2017
Sanity checks for saliency maps
J Adebayo, J Gilmer, M Muelly, I Goodfellow, M Hardt, B Kim
Advances in Neural Information Processing Systems, 9505-9515, 2018
191*2018
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
B Kim, M Wattenberg, J Gilmer, C Cai, J Wexler, F Viegas, R Sayres
arXiv preprint arXiv:1711.11279, 2017
162*2017
Adversarial spheres
J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ...
arXiv preprint arXiv:1801.02774, 2018
1412018
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, 6076-6085, 2017
1322017
Deep information propagation
SS Schoenholz, J Gilmer, S Ganguli, J Sohl-Dickstein
International Conference on Learning Representations 2017, 2016
1252016
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
802018
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
472019
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
29*2016
A new approach to the sensitivity conjecture
J Gilmer, M Koucký, ME Saks
Proceedings of the 2015 Conference on Innovations in Theoretical Computer …, 2015
24*2015
Composition limits and separating examples for some Boolean function complexity measures
J Gilmer, M Saks, S Srinivasan
Combinatorica 36 (3), 265-311, 2016
202016
A fourier perspective on model robustness in computer vision
D Yin, RG Lopes, J Shlens, ED Cubuk, J Gilmer
Advances in Neural Information Processing Systems, 13255-13265, 2019
192019
A local central limit theorem for triangles in a random graph
J Gilmer, S Kopparty
Random Structures & Algorithms 48 (4), 732-750, 2016
15*2016
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
112019
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
92019
Mnist-c: A robustness benchmark for computer vision
N Mu, J Gilmer
arXiv preprint arXiv:1906.02337, 2019
62019
On the Density of Happy Numbers
J Gilmer
INTEGERS 13 (A48), 2013
3*2013
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Articles 1–20