| Deep Neural Networks as Gaussian Processes J Lee*, Y Bahri*, R Novak, SS Schoenholz, J Pennington, ... International Conference on Learning Representations (ICLR), 2018 | 857 | 2018 |
| Wide neural networks of any depth evolve as linear models under gradient descent J Lee*, L Xiao*, SS Schoenholz, Y Bahri, J Sohl-Dickstein, J Pennington Neural Information Processing Systems (NeurIPS), 2019 | 723 | 2019 |
| Measuring the effects of data parallelism on neural network training CJ Shallue*, J Lee*, J Antognini, J Sohl-Dickstein, R Frostig, GE Dahl Journal of Machine Learning Research (2019) 20, 1-49, 2019 | 310 | 2019 |
| Bayesian Deep Convolutional Neural Networks with Many Channels are Gaussian Processes R Novak*, L Xiao*, J Lee, Y Bahri, G Yang, D Abolafia, J Pennington, ... International Conference on Learning Representations (ICLR), 2019 | 260* | 2019 |
| On empirical comparisons of optimizers for deep learning D Choi, CJ Shallue, Z Nado, J Lee, CJ Maddison, GE Dahl arXiv preprint arXiv:1910.05446, 2019 | 226 | 2019 |
| Neural tangents: Fast and easy infinite neural networks in python R Novak*, L Xiao*, J Hron, J Lee, AA Alemi, J Sohl-Dickstein, ... International Conference on Learning Representations (ICLR), Spotlight, 2020 | 183 | 2020 |
| The superconformal bootstrap in three dimensions SM Chester, J Lee, SS Pufu, R Yacoby Journal of High Energy Physics 2014 (9), 1-59, 2014 | 160 | 2014 |
| Exact correlators of BPS operators from the 3d superconformal bootstrap SM Chester, J Lee, SS Pufu, R Yacoby Journal of High Energy Physics 2015 (3), 1-55, 2015 | 143 | 2015 |
| Finite versus infinite neural networks: an empirical study J Lee, SS Schoenholz, J Pennington, B Adlam, L Xiao, R Novak, ... Neural Information Processing Systems (NeurIPS), Spotlight, 2020 | 123 | 2020 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 100 | 2022 |
| Dataset Distillation with Infinitely Wide Convolutional Networks T Nguyen, R Novak, L Xiao, J Lee Neural Information Processing Systems (NeurIPS), 2021 | 73 | 2021 |
| Explaining neural scaling laws Y Bahri*, E Dyer*, J Kaplan*, J Lee*, U Sharma* arXiv preprint arXiv:2102.06701, 2021 | 65 | 2021 |
| Dataset Meta-Learning from Kernel Ridge-Regression T Nguyen, Z Chen, J Lee International Conference on Learning Representations (ICLR), 2021 | 63 | 2021 |
| On the infinite width limit of neural networks with a standard parameterization J Sohl-Dickstein, R Novak, SS Schoenholz, J Lee arXiv preprint arXiv:2001.07301, 2020 | 32 | 2020 |
| 3d minimal SCFTs from wrapped M5-branes JB Bae, D Gang, J Lee Journal of High Energy Physics 2017 (8), 118, 2017 | 30 | 2017 |
| Entanglement entropy from one-point functions in holographic states MJS Beach, J Lee, C Rabideau, M Van Raamsdonk Journal of High Energy Physics 2016 (6), 1-29, 2016 | 29 | 2016 |
| Algebra of Majorana doubling J Lee, F Wilczek Physical Review Letters 111 (22), 226402, 2013 | 29 | 2013 |
| GLSMs for non-Kähler geometries A Adams, E Dyer, J Lee Journal of High Energy Physics 2013 (1), 1-39, 2013 | 28 | 2013 |
| Towards NNGP-guided Neural Architecture Search DS Park*, J Lee*, D Peng, Y Cao, J Sohl-Dickstein arXiv preprint arXiv:2011.06006, 2020 | 20 | 2020 |
| Exploring the Uncertainty Properties of Neural Networks’ Implicit Priors in the Infinite-Width Limit B Adlam*, J Lee*, L Xiao*, J Pennington, J Snoek International Conference on Learning Representations (ICLR), 2021 | 11 | 2021 |
Jascha Sohl-DicksteinGoogle BrainVerified email at google.com
