Deep Neural Networks as Gaussian Processes J Lee*, Y Bahri*, R Novak, SS Schoenholz, J Pennington, ...
International Conference on Learning Representations (ICLR), 2018
1273 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
1081 2019 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, ...
TMLR 2023, 2022
980 2022 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
440 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
377 * 2019 On empirical comparisons of optimizers for deep learning D Choi
arXiv preprint arXiv:1910.05446, 2019
370 2019 Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
300 2024 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
252 2020 Dataset Distillation with Infinitely Wide Convolutional Networks T Nguyen, R Novak, L Xiao, J Lee
Neural Information Processing Systems (NeurIPS), 2021
218 2021 Dataset Meta-Learning from Kernel Ridge-Regression T Nguyen, Z Chen, J Lee
International Conference on Learning Representations (ICLR), 2021
214 2021 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
206 2020 Explaining neural scaling laws Y Bahri*, E Dyer*, J Kaplan*, J Lee*, U Sharma*
arXiv preprint arXiv:2102.06701, 2021
176 2021 The SM Chester, J Lee, SS Pufu, R Yacoby
Journal of High Energy Physics 2014 (9), 1-59, 2014
167 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
150 2015 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
54 2020 Beyond human data: Scaling self-training for problem-solving with language models A Singh, JD Co-Reyes, R Agarwal, A Anand, P Patil, PJ Liu, J Harrison, ...
arXiv preprint arXiv:2312.06585, 2023
41 2023 Algebra of Majorana doubling J Lee, F Wilczek
Physical Review Letters 111 (22), 226402, 2013
37 2013 GLSMs for non-Kähler geometries A Adams, E Dyer, J Lee
Journal of High Energy Physics 2013 (1), 1-39, 2013
34 2013 Towards NNGP-guided Neural Architecture Search DS Park*, J Lee*, D Peng, Y Cao, J Sohl-Dickstein
arXiv preprint arXiv:2011.06006, 2020
33 2020 3d JB Bae, D Gang, J Lee
Journal of High Energy Physics 2017 (8), 118, 2017
31 2017 
Jascha Sohl-DicksteinAnthropicBestätigte E-Mail-Adresse bei anthropic.com
