Justin Gilmer

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 53

Top Topics

Top Venues

Publications

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
Small-scale proxies for large-scale Transformer training instabilities. ICLR (2024)
Mitchell Wortsman, Jaehoon Lee, Justin Gilmer, Simon Kornblith
Replacing softmax with ReLU in Vision Transformers. CoRR (2023)
Jiaxi Tang, Yoel Drori, Daryl Chang, Maheswaran Sathiamoorthy, Justin Gilmer, Li Wei, Xinyang Yi, Lichan Hong, Ed H. Chi
Improving Training Stability for Multitask Ranking Models in Recommender Systems. KDD (2023)
Jiaxi Tang, Yoel Drori, Daryl Chang, Maheswaran Sathiamoorthy, Justin Gilmer, Li Wei, Xinyang Yi, Lichan Hong, Ed H. Chi
Improving Training Stability for Multitask Ranking Models in Recommender Systems. CoRR (2023)
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, Neil Houlsby
Scaling Vision Transformers to 22 Billion Parameters. CoRR (2023)
Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. NeurIPS (2023)
Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani
Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. CoRR (2023)
Mitchell Wortsman, Peter J. Liu, Lechao Xiao, Katie Everett, Alex 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
Small-scale proxies for large-scale Transformer training instabilities. CoRR (2023)
George E. Dahl, Frank Schneider, Zachary Nado, Naman Agarwal, Chandramouli Shama Sastry, Philipp Hennig, Sourabh Medapati, Runa Eschenhagen, Priya Kasimbeg, Daniel Suo, Juhan Bae, Justin Gilmer, Abel L. Peirson, Bilal Khan, Rohan Anil, Mike Rabbat, Shankar Krishnan, Daniel Snider, Ehsan Amid, Kongtao Chen, Chris J. Maddison, Rakshith Vasudev, Michal Badura, Ankush Garg, Peter Mattson
Benchmarking Neural Network Training Algorithms. CoRR (2023)
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
Scaling Vision Transformers to 22 Billion Parameters. ICML (2023)
Derrick Xin, Behrooz Ghorbani, Ankush Garg, Orhan Firat, Justin Gilmer
Do Current Multi-Task Optimization Methods in Deep Learning Even Help? CoRR (2022)
Jeremy M. Cohen, Behrooz Ghorbani, Shankar Krishnan, Naman Agarwal, Sourabh Medapati, Michal Badura, Daniel Suo, David Cardoze, Zachary Nado, George E. Dahl, Justin Gilmer
Adaptive Gradient Methods at the Edge of Stability. CoRR (2022)
Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George Edward Dahl, Zachary Nado, Orhan Firat
A Loss Curvature Perspective on Training Instabilities of Deep Learning Models. ICLR (2022)
Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. AISTATS (2022)
Ryan G. Gomes, Bellington Vwalika, Chace Lee, Angelica Willis, Marcin Sieniek, Joan T. Price, Christina Chen, Margaret P. Kasaro, James A. Taylor, Elizabeth M. Stringer, Scott Mayer McKinney, Ntazana Sindano, George E. Dahl, William Goodnight III, Justin Gilmer, Benjamin H. Chi, Charles Lau, Terry Spitz, T. Saensuksopa, Kris Liu, Jonny Wong, Rory Pilgrim, Akib Uddin, Greg Corrado, Lily Peng, Katherine Chou, Daniel Tse, Jeffrey S. A. Stringer, Shravya Shetty
AI system for fetal ultrasound in low-resource settings. CoRR (2022)
Derrick Xin, Behrooz Ghorbani, Justin Gilmer, Ankush Garg, Orhan Firat
Do Current Multi-Task Optimization Methods in Deep Learning Even Help? NeurIPS (2022)
Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
Pre-training helps Bayesian optimization too. CoRR (2022)
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. ICCV (2021)
Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers. CoRR (2021)
Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl
Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. CoRR (2021)
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 (2021)
Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George E. Dahl, Zachary Nado, Orhan Firat
A Loss Curvature Perspective on Training Instability in Deep Learning. CoRR (2021)
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 (2020)
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)
Nic Ford, Justin Gilmer, Nicholas Carlini, Ekin Dogus Cubuk
Adversarial Examples Are a Natural Consequence of Test Error in Noise. CoRR (2019)
Norman Mu, Justin Gilmer
MNIST-C: A Robustness Benchmark for Computer Vision. CoRR (2019)
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 (2019)
Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, Ekin D. Cubuk
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation. CoRR (2019)
Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin D. Cubuk
Adversarial Examples Are a Natural Consequence of Test Error in Noise. ICML (2019)
Dong Yin, Raphael Gontijo Lopes, Jon Shlens, Ekin Dogus Cubuk, Justin Gilmer
A Fourier Perspective on Model Robustness in Computer Vision. NeurIPS (2019)
Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D. Cubuk, Justin Gilmer
A Fourier Perspective on Model Robustness in Computer Vision. CoRR (2019)
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)
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim
Sanity Checks for Saliency Maps. NeurIPS (2018)
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 (2018)
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 (2018)
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow
Adversarial Spheres. CoRR (2018)
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow
Adversarial Spheres. ICLR (Workshop) (2018)
Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. ICLR (Workshop) (2018)
Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim
Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. CoRR (2018)
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim
Sanity Checks for Saliency Maps. CoRR (2018)
Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein
Deep Information Propagation. ICLR (Poster) (2017)
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
Neural Message Passing for Quantum Chemistry. CoRR (2017)
Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski, David Sussillo
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. ICML (2017)
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
Neural Message Passing for Quantum Chemistry. ICML (2017)
Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement. CoRR (2017)
Justin Gilmer, Michal Koucký, Michael E. Saks
A Communication Game Related to the Sensitivity Conjecture. Theory Comput. 13 (1) (2017)
Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, Justin Gilmer
Adversarial Patch. CoRR (2017)
Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein
Explaining the Learning Dynamics of Direct Feedback Alignment. ICLR (Workshop) (2017)
Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability. NIPS (2017)
Justin Gilmer, Swastik Kopparty
A local central limit theorem for triangles in a random graph. Random Struct. Algorithms 48 (4) (2016)
Jakob N. Foerster, Justin Gilmer, Jan Chorowski, Jascha Sohl-Dickstein, David Sussillo
Intelligible Language Modeling with Input Switched Affine Networks. CoRR (2016)
Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein
Deep Information Propagation. CoRR (2016)
Justin Gilmer, Michael E. Saks, Srikanth Srinivasan
Composition limits and separating examples for some boolean function complexity measures. Comb. 36 (3) (2016)
Justin Gilmer, Michal Koucký, Michael E. Saks
A New Approach to the Sensitivity Conjecture. ITCS (2015)
Justin Gilmer, Michal Koucký, Michael E. Saks
A communication game related to the sensitivity conjecture. CoRR (2015)
Justin Gilmer, Michael E. Saks, Srikanth Srinivasan
Composition limits and separating examples for some Boolean function complexity measures. CoRR (2013)
Justin Gilmer, Michael E. Saks, Srikanth Srinivasan
Composition Limits and Separating Examples for Some Boolean Function Complexity Measures. Computational Complexity Conference (2013)