Gilmer, Justin

20 publications

JMLR 2024 Pre-Trained Gaussian Processes for Bayesian Optimization Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

ICLR 2024 Small-Scale Proxies for Large-Scale Transformer Training Instabilities Mitchell Wortsman, Peter J Liu, Lechao Xiao, Katie E Everett, Alexander A Alemi, Ben Adlam, John D Co-Reyes, Izzeddin Gur, Abhishek Kumar, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein, Kelvin Xu, Jaehoon Lee, Justin Gilmer, Simon Kornblith

NeurIPSW 2023 Adaptive Gradient Methods at the Edge of Stability Jeremy Cohen, Behrooz Ghorbani, Shankar Krishnan, Naman Agarwal, Sourabh Medapati, Michal Badura, Daniel Suo, Zachary Nado, George E. Dahl, Justin Gilmer

NeurIPS 2023 Order Matters in the Presence of Dataset Imbalance for Multilingual Learning Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M Dai, Behrooz Ghorbani

ICML 2023 Scaling Vision Transformers to 22 Billion Parameters Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd Van Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, Neil Houlsby

AISTATS 2022 Predicting the Utility of Search Spaces for Black-Box Optimization: A Simple, Budget-Aware Approach Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George Dahl

ICLR 2022 A Loss Curvature Perspective on Training Instabilities of Deep Learning Models Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George Edward Dahl, Zachary Nado, Orhan Firat

NeurIPS 2022 Do Current Multi-Task Optimization Methods in Deep Learning Even Help? Derrick Xin, Behrooz Ghorbani, Justin Gilmer, Ankush Garg, Orhan Firat

ICCV 2021 The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization 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

ICLR 2020 AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan

Distill 2019 A Discussion of 'Adversarial Examples Are Not Bugs, They Are Features' Logan Engstrom, Justin Gilmer, Gabriel Goh, Dan Hendrycks, Andrew Ilyas, Aleksander Madry, Reiichiro Nakano, Preetum Nakkiran, Shibani Santurkar, Brandon Tran, Dimitris Tsipras, Eric Wallace

NeurIPS 2019 A Fourier Perspective on Model Robustness in Computer Vision Dong Yin, Raphael Gontijo Lopes, Jon Shlens, Ekin Dogus Cubuk, Justin Gilmer

ICML 2019 Adversarial Examples Are a Natural Consequence of Test Error in Noise Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin Cubuk

ICML 2018 Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres

NeurIPS 2018 Sanity Checks for Saliency Maps Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, Been Kim

ICLR 2017 Deep Information Propagation Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein

ICLR 2017 Explaining the Learning Dynamics of Direct Feedback Alignment Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein

ICML 2017 Input Switched Affine Networks: An RNN Architecture Designed for Interpretability Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski, David Sussillo

ICML 2017 Neural Message Passing for Quantum Chemistry Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl

NeurIPS 2017 SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein