Justin Gilmer - RankMe

58 papers A* 14A 3Journal 36Unranked 5

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Pre-trained Gaussian Processes for Bayesian Optimization. J. Mach. Learn. Res. Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
2024	A*	`conf`	Small-scale proxies for large-scale Transformer training instabilities. ICLR 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
2023	J	`jnl`	Benchmarking Neural Network Training Algorithms. CoRR 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
2023	A*	`conf`	Improving Training Stability for Multitask Ranking Models in Recommender Systems. KDD Jiaxi Tang, Yoel Drori, Daryl Chang, Maheswaran Sathiamoorthy, Justin Gilmer, Li Wei, Xinyang Yi, Lichan Hong, Ed H. Chi
2023	J	`jnl`	Improving Training Stability for Multitask Ranking Models in Recommender Systems. CoRR Jiaxi Tang, Yoel Drori, Daryl Chang, Maheswaran Sathiamoorthy, Justin Gilmer, Li Wei, Xinyang Yi, Lichan Hong, Ed H. Chi
2023	A*	`conf`	Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. NeurIPS Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani
2023	J	`jnl`	Order Matters in the Presence of Dataset Imbalance for Multilingual Learning. CoRR Dami Choi, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani
2023	J	`jnl`	Replacing softmax with ReLU in Vision Transformers. CoRR Mitchell Wortsman, Jaehoon Lee, Justin Gilmer, Simon Kornblith
2023	A*	`conf`	Scaling Vision Transformers to 22 Billion Parameters. ICML 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
2023	J	`jnl`	Scaling Vision Transformers to 22 Billion Parameters. CoRR 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
2023	J	`jnl`	Small-scale proxies for large-scale Transformer training instabilities. CoRR 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
2022	A*	`conf`	A Loss Curvature Perspective on Training Instabilities of Deep Learning Models. ICLR Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George Edward Dahl, Zachary Nado, Orhan Firat
2022	J	`jnl`	AI system for fetal ultrasound in low-resource settings. CoRR 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
2022	J	`jnl`	Adaptive Gradient Methods at the Edge of Stability. CoRR Jeremy Cohen, Behrooz Ghorbani, Shankar Krishnan, Naman Agarwal, Sourabh Medapati, Michal Badura, Daniel Suo, David Cardoze, Zachary Nado, George E. Dahl, Justin Gilmer
2022	A*	`conf`	Do Current Multi-Task Optimization Methods in Deep Learning Even Help? NeurIPS Derrick Xin, Behrooz Ghorbani, Justin Gilmer, Ankush Garg, Orhan Firat
2022	J	`jnl`	Do Current Multi-Task Optimization Methods in Deep Learning Even Help? CoRR Derrick Xin, Behrooz Ghorbani, Ankush Garg, Orhan Firat, Justin Gilmer
2022	J	`jnl`	Pre-training helps Bayesian optimization too. CoRR Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
2022	A	`conf`	Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. AISTATS Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl
2021	J	`jnl`	A Large Batch Optimizer Reality Check: Traditional, Generic Optimizers Suffice Across Batch Sizes. CoRR Zachary Nado, Justin Gilmer, Christopher J. Shallue, Rohan Anil, George E. Dahl
2021	J	`jnl`	A Loss Curvature Perspective on Training Instability in Deep Learning. CoRR Justin Gilmer, Behrooz Ghorbani, Ankush Garg, Sneha Kudugunta, Behnam Neyshabur, David Cardoze, George E. Dahl, Zachary Nado, Orhan Firat
2021	J	`jnl`	Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers. CoRR Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zachary Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani
2021	J	`jnl`	Predicting the utility of search spaces for black-box optimization: a simple, budget-aware approach. CoRR Setareh Ariafar, Justin Gilmer, Zachary Nado, Jasper Snoek, Rodolphe Jenatton, George E. Dahl
2021	A*	`conf`	The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. ICCV 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
2020	A*	`conf`	AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. ICLR Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan
2020	J	`jnl`	The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization. CoRR 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
2019	A*	`conf`	A Fourier Perspective on Model Robustness in Computer Vision. NeurIPS Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin Dogus Cubuk, Justin Gilmer
2019	J	`jnl`	A Fourier Perspective on Model Robustness in Computer Vision. CoRR Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D. Cubuk, Justin Gilmer
2019	A*	`conf`	Adversarial Examples Are a Natural Consequence of Test Error in Noise. ICML Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin D. Cubuk
2019	J	`jnl`	Adversarial Examples Are a Natural Consequence of Test Error in Noise. CoRR Nic Ford, Justin Gilmer, Nicholas Carlini, Ekin Dogus Cubuk
2019	J	`jnl`	AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. CoRR Dan Hendrycks, Norman Mu, Ekin D. Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan
2019	J	`jnl`	Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation. CoRR Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, Ekin D. Cubuk
2019	J	`jnl`	MNIST-C: A Robustness Benchmark for Computer Vision. CoRR Norman Mu, Justin Gilmer
2018	—	`conf`	Adversarial Spheres. ICLR (Workshop) Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow
2018	J	`jnl`	Adversarial Spheres. CoRR Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow
2018	A*	`conf`	Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). ICML Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J. Cai, James Wexler, Fernanda B. Viégas, Rory Sayres
2018	—	`conf`	Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. ICLR (Workshop) Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim
2018	J	`jnl`	Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. CoRR Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim
2018	J	`jnl`	Motivating the Rules of the Game for Adversarial Example Research. CoRR Justin Gilmer, Ryan P. Adams, Ian J. Goodfellow, David G. Andersen, George E. Dahl
2018	J	`jnl`	Relational inductive biases, deep learning, and graph networks. CoRR 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
2018	A*	`conf`	Sanity Checks for Saliency Maps. NeurIPS Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim
2018	J	`jnl`	Sanity Checks for Saliency Maps. CoRR Julius Adebayo, Justin Gilmer, Michael Muelly, Ian J. Goodfellow, Moritz Hardt, Been Kim
2017	J	`jnl`	A Communication Game Related to the Sensitivity Conjecture. Theory Comput. Justin Gilmer, Michal Koucký, Michael E. Saks
2017	J	`jnl`	Adversarial Patch. CoRR Tom B. Brown, Dandelion Mané, Aurko Roy, Martín Abadi, Justin Gilmer
2017	—	`conf`	Deep Information Propagation. ICLR (Poster) Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein
2017	—	`conf`	Explaining the Learning Dynamics of Direct Feedback Alignment. ICLR (Workshop) Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein
2017	A*	`conf`	Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. ICML Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski, David Sussillo
2017	A*	`conf`	Neural Message Passing for Quantum Chemistry. ICML Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
2017	J	`jnl`	Neural Message Passing for Quantum Chemistry. CoRR Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
2017	—	`conf`	SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability. NIPS Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein
2017	J	`jnl`	SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement. CoRR Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein
2016	J	`jnl`	A local central limit theorem for triangles in a random graph. Random Struct. Algorithms Justin Gilmer, Swastik Kopparty
2016	J	`jnl`	Composition limits and separating examples for some boolean function complexity measures. Comb. Justin Gilmer, Michael E. Saks, Srikanth Srinivasan
2016	J	`jnl`	Deep Information Propagation. CoRR Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein
2016	J	`jnl`	Intelligible Language Modeling with Input Switched Affine Networks. CoRR Jakob N. Foerster, Justin Gilmer, Jan Chorowski, Jascha Sohl-Dickstein, David Sussillo
2015	A	`conf`	A New Approach to the Sensitivity Conjecture. ITCS Justin Gilmer, Michal Koucký, Michael E. Saks
2015	J	`jnl`	A communication game related to the sensitivity conjecture. CoRR Justin Gilmer, Michal Koucký, Michael E. Saks
2013	A	`conf`	Composition Limits and Separating Examples for Some Boolean Function Complexity Measures. CCC Justin Gilmer, Michael E. Saks, Srikanth Srinivasan
2013	J	`jnl`	Composition limits and separating examples for some Boolean function complexity measures. CoRR Justin Gilmer, Michael E. Saks, Srikanth Srinivasan