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2020 – today
- 2024
[j4]Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani:
Pre-trained Gaussian Processes for Bayesian Optimization. J. Mach. Learn. Res. 25: 212:1-212:83 (2024)- [c22]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 - 2023
- [c21]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: 7480-7512 - [c20]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: 4882-4893 - [c19]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 - [i32]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 Nader 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 abs/2302.05442 (2023) - [i31]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 abs/2302.09178 (2023) - [i30]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 abs/2306.07179 (2023) - [i29]Mitchell Wortsman, Jaehoon Lee, Justin Gilmer, Simon Kornblith:
Replacing softmax with ReLU in Vision Transformers. CoRR abs/2309.08586 (2023) - [i28]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 abs/2309.14322 (2023) - [i27]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 abs/2312.06134 (2023) - 2022
- [c18]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: 11056-11071 - [c17]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 - [c16]Derrick Xin, Behrooz Ghorbani, Justin Gilmer, Ankush Garg, Orhan Firat:
Do Current Multi-Task Optimization Methods in Deep Learning Even Help? NeurIPS 2022 - [i26]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 abs/2203.10139 (2022) - [i25]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 abs/2207.03084 (2022) - [i24]Jeremy 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 abs/2207.14484 (2022) - [i23]Derrick Xin, Behrooz Ghorbani, Ankush Garg, Orhan Firat, Justin Gilmer:
Do Current Multi-Task Optimization Methods in Deep Learning Even Help? CoRR abs/2209.11379 (2022) - 2021
- [c15]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: 8320-8329 - [i22]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) - [i21]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 abs/2109.08215 (2021) - [i20]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 abs/2110.04369 (2021) - [i19]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 abs/2112.08250 (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, Jonathon 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 M. 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. CCC 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
George E. Dahl
aka: George Edward Dahl
aka: George Edward Dahl
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