| 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, ... arXiv preprint arXiv:1902.06720, 2019 | 290 | 2019 |
| 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 | 152 | 2018 |
| Bayesian Deep Convolutional Neural Networks with Many Channels are Gaussian Processes R Novak, L Xiao, Y Bahri, J Lee, G Yang, DA Abolafia, J Pennington, ... | 124* | 2018 |
| Neural tangents: Fast and easy infinite neural networks in python R Novak, L Xiao, J Hron, J Lee, AA Alemi, J Sohl-Dickstein, ... arXiv preprint arXiv:1912.02803, 2019 | 36 | 2019 |
| Uniform estimates for bilinear Hilbert transforms and bilinear maximal functions associated to polynomials X Li, L Xiao American Journal of Mathematics 138 (4), 907-962, 2016 | 23 | 2016 |
| Provable benefit of orthogonal initialization in optimizing deep linear networks W Hu, L Xiao, J Pennington arXiv preprint arXiv:2001.05992, 2020 | 22 | 2020 |
| Maximal decay inequalities for trilinear oscillatory integrals of convolution type PT Gressman, L Xiao Journal of Functional Analysis 271 (12), 3695-3726, 2016 | 17 | 2016 |
| Endpoint estimates for one-dimensional oscillatory integral operators L Xiao Advances in Mathematics 316, 255-291, 2017 | 15 | 2017 |
| Disentangling trainability and generalization in deep learning L Xiao, J Pennington, S Schoenholz | 14 | 2019 |
| Bilinear Hilbert transforms associated with plane curves J Guo, L Xiao The Journal of Geometric Analysis 26 (2), 967-995, 2016 | 12 | 2016 |
| Finite versus infinite neural networks: an empirical study J Lee, SS Schoenholz, J Pennington, B Adlam, L Xiao, R Novak, ... arXiv preprint arXiv:2007.15801, 2020 | 11 | 2020 |
| Sharp estimates for trilinear oscillatory integrals and an algorithm of two-dimensional resolution of singularities L Xiao arXiv preprint arXiv:1311.3725, 2013 | 9* | 2013 |
| Higher decay inequalities for multilinear oscillatory integrals M Gilula, PT Gressman, L Xiao arXiv preprint arXiv:1612.00050, 2016 | 8 | 2016 |
| Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. arXiv e-prints, art J Lee, L Xiao, SS Schoenholz, Y Bahri, R Novak, J Sohl-Dickstein, ... arXiv preprint arXiv:1902.06720, 2019 | 5 | 2019 |
| Neural tangents: Fast and easy infinite neural networks in python, 2019 R Novak, L Xiao, J Hron, J Lee, AA Alemi, J Sohl-Dickstein, ... URL http://github. com/google/neural-tangents, 0 | 5 | |
| 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 | 4 | 2020 |
| The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks W Hu, L Xiao, B Adlam, J Pennington arXiv preprint arXiv:2006.14599, 2020 | 2 | 2020 |
| Disentangling Trainability and Generalization in Deep Neural Networks L Xiao, J Pennington, S Schoenholz International Conference on Machine Learning, 10462-10472, 2020 | 1 | 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 arXiv preprint arXiv:2010.07355, 2020 | 1 | 2020 |
| Oscillatory Loomis-Whitney and Projections of Sublevel Sets M Gilula, K O'Neill, L Xiao arXiv preprint arXiv:1903.12300, 2019 | 1 | 2019 |
Jeffrey PenningtonGoogle BrainVerified email at google.com
