| Neural message passing for quantum chemistry J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl International Conference on Machine Learning 2017, 1263-1272, 2017 | 9685 | 2017 |
| Relational inductive biases, deep learning, and graph networks PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ... arXiv preprint arXiv:1806.01261, 2018 | 4067 | 2018 |
| Sanity checks for saliency maps J Adebayo, J Gilmer, M Muelly, I Goodfellow, M Hardt, B Kim Advances in neural information processing systems 31, 2018 | 2569 | 2018 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2510 | 2023 |
| Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav) B Kim, M Wattenberg, J Gilmer, C Cai, J Wexler, F Viegas International conference on machine learning, 2668-2677, 2018 | 2260 | 2018 |
| The many faces of robustness: A critical analysis of out-of-distribution generalization D Hendrycks, S Basart, N Mu, S Kadavath, F Wang, E Dorundo, R Desai, ... Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 1795 | 2021 |
| Augmix: A simple data processing method to improve robustness and uncertainty D Hendrycks, N Mu, ED Cubuk, B Zoph, J Gilmer, B Lakshminarayanan arXiv preprint arXiv:1912.02781, 2019 | 1503 | 2019 |
| Adversarial patch TB Brown, D Mané, A Roy, M Abadi, J Gilmer Advances in Neural Information Processing Systems (Workshop Track), 2017 | 1117 | 2017 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 989 | 2024 |
| Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability M Raghu, J Gilmer, J Yosinski, J Sohl-Dickstein Advances in neural information processing systems 30, 2017 | 773 | 2017 |
| Prediction errors of molecular machine learning models lower than hybrid DFT error FA Faber, L Hutchison, B Huang, J Gilmer, SS Schoenholz, GE Dahl, ... Journal of chemical theory and computation 13 (11), 5255-5264, 2017 | 733* | 2017 |
| A fourier perspective on model robustness in computer vision D Yin, R Gontijo Lopes, J Shlens, ED Cubuk, J Gilmer Advances in Neural Information Processing Systems 32, 2019 | 571 | 2019 |
| Scaling vision transformers to 22 billion parameters M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ... International Conference on Machine Learning, 7480-7512, 2023 | 538 | 2023 |
| Deep information propagation SS Schoenholz, J Gilmer, S Ganguli, J Sohl-Dickstein International Conference on Learning Representations 2017, 2016 | 453 | 2016 |
| Adversarial spheres J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ... arXiv preprint arXiv:1801.02774, 2018 | 423 | 2018 |
| Motivating the rules of the game for adversarial example research J Gilmer, RP Adams, I Goodfellow, D Andersen, GE Dahl arXiv preprint arXiv:1807.06732, 2018 | 260 | 2018 |
| Improving robustness without sacrificing accuracy with patch gaussian augmentation RG Lopes, D Yin, B Poole, J Gilmer, ED Cubuk arXiv preprint arXiv:1906.02611, 2019 | 240 | 2019 |
| Relational inductive biases, deep learning, and graph networks. arXiv 2018 PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ... arXiv preprint arXiv:1806.01261, 2018 | 232 | 2018 |
| Mnist-c: A robustness benchmark for computer vision N Mu, J Gilmer arXiv preprint arXiv:1906.02337, 2019 | 227 | 2019 |
| Adversarial examples are a natural consequence of test error in noise J Gilmer, N Ford, N Carlini, E Cubuk International Conference on Machine Learning, 2280-2289, 2019 | 193 | 2019 |
George E. DahlGoogle Inc.Verified email at google.com