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Triple descent and the two kinds of overfitting: Where & why do they appear?


Jun 05, 2020
St├ęphane d'Ascoli, Levent Sagun, Giulio Biroli


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On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks


Nov 29, 2019
Umut ┼×im┼čekli, Mert G├╝rb├╝zbalaban, Thanh Huy Nguyen, Ga├źl Richard, Levent Sagun

* 32 pages. arXiv admin note: substantial text overlap with arXiv:1901.06053 

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Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias


Jun 16, 2019
St├ęphane d'Ascoli, Levent Sagun, Joan Bruna, Giulio Biroli


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Scaling description of generalization with number of parameters in deep learning


Jan 18, 2019
Mario Geiger, Arthur Jacot, Stefano Spigler, Franck Gabriel, Levent Sagun, St├ęphane d'Ascoli, Giulio Biroli, Cl├ęment Hongler, Matthieu Wyart


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A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks


Jan 18, 2019
Umut Simsekli, Levent Sagun, Mert Gurbuzbalaban


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A jamming transition from under- to over-parametrization affects loss landscape and generalization


Oct 22, 2018
Stefano Spigler, Mario Geiger, St├ęphane d'Ascoli, Levent Sagun, Giulio Biroli, Matthieu Wyart

* 11 pages, 6 figures, submitted to NIPS workshop "Integration of Deep Learning Theories". arXiv admin note: substantial text overlap with arXiv:1809.09349 

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The jamming transition as a paradigm to understand the loss landscape of deep neural networks


Oct 03, 2018
Mario Geiger, Stefano Spigler, St├ęphane d'Ascoli, Levent Sagun, Marco Baity-Jesi, Giulio Biroli, Matthieu Wyart


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Empirical Analysis of the Hessian of Over-Parametrized Neural Networks


May 07, 2018
Levent Sagun, Utku Evci, V. Ugur Guney, Yann Dauphin, Leon Bottou

* Minor update for ICLR 2018 Workshop Track presentation 

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Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond


Oct 05, 2017
Levent Sagun, Leon Bottou, Yann LeCun

* ICLR submission, 2016 - updated to match the openreview.net version 

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SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine


Jun 11, 2017
Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Guney, Volkan Cirik, Kyunghyun Cho


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Entropy-SGD: Biasing Gradient Descent Into Wide Valleys


Apr 21, 2017
Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer Chayes, Levent Sagun, Riccardo Zecchina

* ICLR '17 

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Perspective: Energy Landscapes for Machine Learning


Mar 23, 2017
Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, David J. Wales

* 41 pages, 25 figures. Accepted for publication in Physical Chemistry Chemical Physics, 2017 

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Universal halting times in optimization and machine learning


Feb 21, 2017
Levent Sagun, Thomas Trogdon, Yann LeCun


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Explorations on high dimensional landscapes


Apr 06, 2015
Levent Sagun, V. Ugur Guney, Gerard Ben Arous, Yann LeCun

* 11 pages, 8 figures, workshop contribution at ICLR 2015 

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