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Explaining Neural Scaling Laws

Feb 12, 2021
Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, Utkarsh Sharma

* 11 pages, 5 figures + Supplement 

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Towards NNGP-guided Neural Architecture Search

Nov 11, 2020
Daniel S. Park, Jaehoon Lee, Daiyi Peng, Yuan Cao, Jascha Sohl-Dickstein

* 13 + 6 pages, 19 figures; open-source code available at 

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Dataset Meta-Learning from Kernel Ridge-Regression

Oct 30, 2020
Timothy Nguyen, Zhourung Chen, Jaehoon Lee

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Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit

Oct 14, 2020
Ben Adlam, Jaehoon Lee, Lechao Xiao, Jeffrey Pennington, Jasper Snoek

* 23 pages, 11 figures 

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Finite Versus Infinite Neural Networks: an Empirical Study

Sep 08, 2020
Jaehoon Lee, Samuel S. Schoenholz, Jeffrey Pennington, Ben Adlam, Lechao Xiao, Roman Novak, Jascha Sohl-Dickstein

* 17+11 pages; v2 references added, minor improvements 

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Data-Efficient Deep Learning Method for Image Classification Using Data Augmentation, Focal Cosine Loss, and Ensemble

Jul 15, 2020
Byeongjo Kim, Chanran Kim, Jaehoon Lee, Jein Song, Gyoungsoo Park

* 7 pages, 2 figures, technical report of 1st Visual Inductive Priors for Data-Efficient Deep Learning Workshop Challenge in ECCV 2020 

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NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results

May 06, 2020
Dario Fuoli, Zhiwu Huang, Martin Danelljan, Radu Timofte, Hua Wang, Longcun Jin, Dewei Su, Jing Liu, Jaehoon Lee, Michal Kudelski, Lukasz Bala, Dmitry Hrybov, Marcin Mozejko, Muchen Li, Siyao Li, Bo Pang, Cewu Lu, Chao Li, Dongliang He, Fu Li, Shilei Wen

* The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 

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On the infinite width limit of neural networks with a standard parameterization

Jan 25, 2020
Jascha Sohl-Dickstein, Roman Novak, Samuel S. Schoenholz, Jaehoon Lee

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Neural Tangents: Fast and Easy Infinite Neural Networks in Python

Dec 05, 2019
Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz

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On Empirical Comparisons of Optimizers for Deep Learning

Oct 11, 2019
Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl

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Physics Enhanced Artificial Intelligence

Mar 11, 2019
Patrick O'Driscoll, Jaehoon Lee, Bo Fu

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Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent

Feb 18, 2019
Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Jascha Sohl-Dickstein, Jeffrey Pennington

* 10+8 pages, 13 figures 

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Measuring the Effects of Data Parallelism on Neural Network Training

Nov 21, 2018
Christopher J. Shallue, Jaehoon Lee, Joseph Antognini, Jascha Sohl-Dickstein, Roy Frostig, George E. Dahl

* Submitted to JMLR 

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Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes

Oct 11, 2018
Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein

* 26 pages, 7 figures 

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Deep Neural Networks as Gaussian Processes

Mar 03, 2018
Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein

* Published version in ICLR 2018. 10 pages + appendix 

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