Justin Gilmer
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
15
Venues
5
Active years
2017–2024
Best venue rank
A*
Where they publish
Papers
15 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2024 | ICLR | 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 |
| 2023 | ICML | 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 |
| 2023 | KDD | Improving Training Stability for Multitask Ranking Models in Recommender Systems. | Jiaxi Tang, Yoel Drori, Daryl Chang, Maheswaran Sathiamoorthy, Justin Gilmer, Li Wei, Xinyang Yi, Lichan Hong, Ed H. Chi |
| 2022 | AISTATS | 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 E. Dahl |
| 2022 | ICLR | 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 |
| 2021 | ICCV | 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 |
| 2020 | ICLR | AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty. | Dan Hendrycks, Norman Mu, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, Balaji Lakshminarayanan |
| 2019 | ICML | Adversarial Examples Are a Natural Consequence of Test Error in Noise. | Justin Gilmer, Nicolas Ford, Nicholas Carlini, Ekin D. Cubuk |
| 2018 | ICLR | Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values. | Julius Adebayo, Justin Gilmer, Ian J. Goodfellow, Been Kim |
| 2018 | ICLR | Adversarial Spheres. | Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian J. Goodfellow |
| 2018 | ICML | Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV). | Been Kim, Martin Wattenberg, Justin Gilmer, Carrie J. Cai, James Wexler, Fernanda B. Vigas, Rory Sayres |
| 2017 | ICLR | Explaining the Learning Dynamics of Direct Feedback Alignment. | Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein |
| 2017 | ICLR | Deep Information Propagation. | Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein |
| 2017 | ICML | Input Switched Affine Networks: An RNN Architecture Designed for Interpretability. | Jakob N. Foerster, Justin Gilmer, Jascha Sohl-Dickstein, Jan Chorowski, David Sussillo |
| 2017 | ICML | Neural Message Passing for Quantum Chemistry. | Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl |