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ORCIDJustin Gilmer
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2020 – today
- 2021
[i19]Zachary Nado, Justin Gilmer, Christopher J. Shallue, Rohan Anil, George E. Dahl:
A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes. CoRR abs/2102.06356 (2021)- 2020
- [c14]Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan:
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. ICLR 2020 - [i18]Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, Justin Gilmer:
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. CoRR abs/2006.16241 (2020)
2010 – 2019
- 2019
- [c13]Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin D. Cubuk:
Adversarial Examples Are a Natural Consequence of Test Error in Noise. ICML 2019: 2280-2289 - [c12]Dong Yin, Raphael Gontijo Lopes, Jon Shlens, Ekin Dogus Cubuk, Justin Gilmer:
A Fourier Perspective on Model Robustness in Computer Vision. NeurIPS 2019: 13255-13265 - [i17]Nic Ford, Justin Gilmer, Nicholas Carlini, Ekin Dogus Cubuk:
Adversarial Examples Are a Natural Consequence of Test Error in Noise. CoRR abs/1901.10513 (2019) - [i16]Norman Mu, Justin Gilmer:
MNIST-C: A Robustness Benchmark for Computer Vision. CoRR abs/1906.02337 (2019) - [i15]Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, Ekin D. Cubuk:
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation. CoRR abs/1906.02611 (2019) - [i14]Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D. Cubuk, Justin Gilmer:
A Fourier Perspective on Model Robustness in Computer Vision. CoRR abs/1906.08988 (2019) - [i13]Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan:
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. CoRR abs/1912.02781 (2019) - 2018
- [c11]Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim:
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. ICLR (Workshop) 2018 - [c10]Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow:
Adversarial Spheres. ICLR (Workshop) 2018 - [c9]Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J. Cai, James Wexler, Fernanda B. Viégas, Rory Sayres:
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). ICML 2018: 2673-2682 - [c8]Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim:
Sanity Checks for Saliency Maps. NeurIPS 2018: 9525-9536 - [i12]Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow:
Adversarial Spheres. CoRR abs/1801.02774 (2018) - [i11]Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinícius Flores Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Çaglar Gülçehre, H. Francis Song, Andrew J. Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey R. Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matthew Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu:
Relational inductive biases, deep learning, and graph networks. CoRR abs/1806.01261 (2018) - [i10]Justin Gilmer, Ryan P. Adams, Ian J. Goodfellow, David G. Andersen, George E. Dahl:
Motivating the Rules of the Game for Adversarial Example Research. CoRR abs/1807.06732 (2018) - [i9]Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim:
Sanity Checks for Saliency Maps. CoRR abs/1810.03292 (2018) - [i8]Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim:
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. CoRR abs/1810.03307 (2018) - 2017
- [j3]Justin Gilmer, Michal Koucký, Michael E. Saks:
A Communication Game Related to the Sensitivity Conjecture. Theory Comput. 13(1): 1-18 (2017) - [c7]Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein:
Explaining the Learning Dynamics of Direct Feedback Alignment. ICLR (Workshop) 2017 - [c6]Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein:
Deep Information Propagation. ICLR (Poster) 2017 - [c5]Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski
, David Sussillo:
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. ICML 2017: 1136-1145 - [c4]Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl:
Neural Message Passing for Quantum Chemistry. ICML 2017: 1263-1272 - [c3]Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein:
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability. NIPS 2017: 6076-6085 - [i7]Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl:
Neural Message Passing for Quantum Chemistry. CoRR abs/1704.01212 (2017) - [i6]Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein:
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement. CoRR abs/1706.05806 (2017) - [i5]Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, Justin Gilmer:
Adversarial Patch. CoRR abs/1712.09665 (2017) - 2016
- [j2]Justin Gilmer, Michael E. Saks, Srikanth Srinivasan:
Composition limits and separating examples for some boolean function complexity measures. Comb. 36(3): 265-311 (2016) - [j1]Justin Gilmer, Swastik Kopparty:
A local central limit theorem for triangles in a random graph. Random Struct. Algorithms 48(4): 732-750 (2016) - [i4]Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein:
Deep Information Propagation. CoRR abs/1611.01232 (2016) - [i3]Jakob N. Foerster, Justin Gilmer, Jan Chorowski, Jascha Sohl-Dickstein, David Sussillo:
Intelligible Language Modeling with Input Switched Affine Networks. CoRR abs/1611.09434 (2016) - 2015
- [c2]Justin Gilmer, Michal Koucký, Michael E. Saks:
A New Approach to the Sensitivity Conjecture. ITCS 2015: 247-254 - [i2]Justin Gilmer, Michal Koucký, Michael E. Saks:
A communication game related to the sensitivity conjecture. CoRR abs/1511.07729 (2015) - 2013
- [c1]Justin Gilmer, Michael E. Saks, Srikanth Srinivasan:
Composition Limits and Separating Examples for Some Boolean Function Complexity Measures. Computational Complexity Conference 2013: 185-196 - [i1]Justin Gilmer, Michael E. Saks, Srikanth Srinivasan:
Composition limits and separating examples for some Boolean function complexity measures. CoRR abs/1306.0630 (2013)
Coauthor Index
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