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The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization

Jun 29, 2020
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

* Datasets, code, and models available at https://github.com/hendrycks/imagenet-r 

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AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Dec 05, 2019
Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan

* Code available at https://github.com/google-research/augmix 

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A Fourier Perspective on Model Robustness in Computer Vision

Jun 21, 2019
Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D. Cubuk, Justin Gilmer


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Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation

Jun 06, 2019
Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, Ekin D. Cubuk


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MNIST-C: A Robustness Benchmark for Computer Vision

Jun 05, 2019
Norman Mu, Justin Gilmer


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Adversarial Examples Are a Natural Consequence of Test Error in Noise

Jan 29, 2019
Nic Ford, Justin Gilmer, Nicolas Carlini, Dogus Cubuk


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Sanity Checks for Saliency Maps

Oct 28, 2018
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, Been Kim

* NIPS 2018 Camera Ready Version 

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Relational inductive biases, deep learning, and graph networks

Oct 17, 2018
Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andrew Ballard, Justin Gilmer, George Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, Razvan Pascanu


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Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values

Oct 08, 2018
Julius Adebayo, Justin Gilmer, Ian Goodfellow, Been Kim

* Workshop Track International Conference on Learning Representations (ICLR) 

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Adversarial Spheres

Sep 10, 2018
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian Goodfellow


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Motivating the Rules of the Game for Adversarial Example Research

Jul 20, 2018
Justin Gilmer, Ryan P. Adams, Ian Goodfellow, David Andersen, George E. Dahl


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Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

Jun 07, 2018
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres


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Adversarial Patch

May 17, 2018
Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, Justin Gilmer


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SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability

Nov 08, 2017
Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein

* Accepted to NIPS 2017, code: https://github.com/google/svcca/ , new plots on Imagenet 

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Neural Message Passing for Quantum Chemistry

Jun 12, 2017
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl

* 14 pages 

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Input Switched Affine Networks: An RNN Architecture Designed for Interpretability

Jun 12, 2017
Jakob N. Foerster, Justin Gilmer, Jan Chorowski, Jascha Sohl-Dickstein, David Sussillo

* ICLR 2107 submission: https://openreview.net/forum?id=H1MjAnqxg 

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Deep Information Propagation

Apr 04, 2017
Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein


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