Samuel S. Schoenholz
Samuel S. Schoenholz
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Cited by
Cited by
Neural message passing for quantum chemistry
J Gilmer, SS Schoenholz, PF Riley, O Vinyals, GE Dahl
International conference on machine learning, 1263-1272, 2017
Deep neural networks as gaussian processes
J Lee, Y Bahri, R Novak, SS Schoenholz, J Pennington, J Sohl-Dickstein
International Conference on Learning Representations, 2017
Wide neural networks of any depth evolve as linear models under gradient descent
J Lee, L Xiao, S Schoenholz, Y Bahri, R Novak, J Sohl-Dickstein, ...
Advances in neural information processing systems 32, 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, ...
arXiv preprint arXiv:2206.04615, 2022
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, 2017
A structural approach to relaxation in glassy liquids
SS Schoenholz, ED Cubuk, DM Sussman, E Kaxiras, AJ Liu
Nature Physics 12, 469-471, 2016
Identifying structural flow defects in disordered solids using machine-learning methods
ED Cubuk, SS Schoenholz, JM Rieser, BD Malone, J Rottler, DJ Durian, ...
Physical review letters 114 (10), 108001, 2015
Adversarial spheres
J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ...
arXiv preprint arXiv:1801.02774, 2018
Deep information propagation
SS Schoenholz, J Gilmer, S Ganguli, J Sohl-Dickstein
International Conference on Learning Representations, 2016
Dynamical isometry and a mean field theory of cnns: How to train 10,000-layer vanilla convolutional neural networks
L Xiao, Y Bahri, J Sohl-Dickstein, S Schoenholz, J Pennington
International Conference on Machine Learning, 5393-5402, 2018
Unveiling the predictive power of static structure in glassy systems
V Bapst, T Keck, A Grabska-Barwińska, C Donner, ED Cubuk, ...
Nature physics 16 (4), 448-454, 2020
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice
J Pennington, S Schoenholz, S Ganguli
Advances in neural information processing systems 30, 2017
Structure-property relationships from universal signatures of plasticity in disordered solids
ED Cubuk, RJS Ivancic, SS Schoenholz, DJ Strickland, A Basu, ...
Science 358 (6366), 1033-1037, 2017
Statistical mechanics of deep learning
Y Bahri, J Kadmon, J Pennington, SS Schoenholz, J Sohl-Dickstein, ...
Annual Review of Condensed Matter Physics 11, 501-528, 2020
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 (Spotlight), 2019
Jax, MD: A framework for differentiable physics
S Schoenholz, ED Cubuk
Advances in Neural Information Processing Systems (Spotlight) 33, 2020
Mean Field Residual Networks: On the Edge of Chaos
G Yang, SS Schoenholz
Advances in neural information processing systems, 2017
Scaling deep learning for materials discovery
A Merchant, S Batzner, SS Schoenholz, M Aykol, G Cheon, ED Cubuk
Nature 624 (7990), 80-85, 2023
A mean field theory of batch normalization
G Yang, J Pennington, V Rao, J Sohl-Dickstein, SS Schoenholz
International Conference on Learning Representations, 2019
Finite versus infinite neural networks: an empirical study
J Lee, SS Schoenholz, J Pennington, B Adlam, L Xiao, R Novak, ...
Advances in neural information processing systems (Spotlight), 2020
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