Jaehoon Lee
Title
Cited by
Cited by
Year
Deep Neural Networks as Gaussian Processes
J Lee*, Y Bahri*, R Novak, SS Schoenholz, J Pennington, ...
International Conference on Learning Representations (ICLR) 2018, 2018
3702018
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, 2019
2522019
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
1432019
The superconformal bootstrap in three dimensions
SM Chester, J Lee, SS Pufu, R Yacoby
Journal of High Energy Physics 2014 (9), 143, 2014
1312014
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, 2019
105*2019
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), 130, 2015
1052015
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 onLearning Representations (ICLR) 2020, 2020
322020
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
312019
Algebra of Majorana doubling
J Lee, F Wilczek
Physical Review Letters 111 (22), 226402, 2013
272013
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), 85, 2016
262016
GLSMs for non-Kähler geometries
A Adams, E Dyer, J Lee
Journal of High Energy Physics 2013 (1), 44, 2013
262013
3d minimal SCFTs from wrapped M5-branes
JB Bae, D Gang, J Lee
Journal of High Energy Physics 2017 (8), 118, 2017
222017
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) 2020, 2020
82020
Linking dynamical heterogeneity to static amorphous order
P Charbonneau, E Dyer, J Lee, S Yaida
Journal of Statistical Mechanics: Theory and Experiment 2016 (7), 074004, 2016
82016
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
62020
Glassy slowdown and replica-symmetry-breaking instantons
A Adams, T Anous, J Lee, S Yaida
Physical Review E 91 (3), 032148, 2015
42015
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, 2020
12020
Wide neural networks of any depth evolve as linear models under gradient descent
J Lee, L Xiao, SS Schoenholz, Y Bahri, R Novak, J Sohl-Dickstein, ...
Journal of Statistical Mechanics: Theory and Experiment 2020 (12), 124002, 2020
2020
Towards NNGP-guided Neural Architecture Search
DS Park*, J Lee*, D Peng, Y Cao, J Sohl-Dickstein
arXiv preprint arXiv:2011.06006, 2020
2020
Dataset Meta-Learning from Kernel Ridge-Regression
T Nguyen, Z Chen, J Lee
International Conference on Learning Representations (ICLR) 2021, 2020
2020
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Articles 1–20