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The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning


Jun 30, 2021
Anders Andreassen, Yasaman Bahri, Behnam Neyshabur, Rebecca Roelofs

* 27 pages, 25 figures 

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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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Exact posterior distributions of wide Bayesian neural networks


Jun 18, 2020
Jiri Hron, Yasaman Bahri, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein


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Infinite attention: NNGP and NTK for deep attention networks


Jun 18, 2020
Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak

* ICML 2020 

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The large learning rate phase of deep learning: the catapult mechanism


Mar 04, 2020
Aitor Lewkowycz, Yasaman Bahri, Ethan Dyer, Jascha Sohl-Dickstein, Guy Gur-Ari

* 25 pages, 19 figures 

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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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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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Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks


Jul 10, 2018
Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington

* ICML 2018 Conference Proceedings 

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Sensitivity and Generalization in Neural Networks: an Empirical Study


Jun 18, 2018
Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein

* Published as a conference paper at ICLR 2018 

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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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