Levent Sagun
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Entropy-sgd: Biasing gradient descent into wide valleys
P Chaudhari, A Choromanska, S Soatto, Y LeCun, C Baldassi, C Borgs, ...
arXiv preprint arXiv:1611.01838, 2016
Searchqa: A new q&a dataset augmented with context from a search engine
M Dunn, L Sagun, M Higgins, VU Guney, V Cirik, K Cho
arXiv preprint arXiv:1704.05179, 2017
Empirical analysis of the hessian of over-parametrized neural networks
L Sagun, U Evci, VU Guney, Y Dauphin, L Bottou
arXiv preprint arXiv:1706.04454, 2017
Eigenvalues of the hessian in deep learning: Singularity and beyond
L Sagun, L Bottou, Y LeCun
arXiv preprint arXiv:1611.07476, 2016
Energy landscapes for machine learning
AJ Ballard, R Das, S Martiniani, D Mehta, L Sagun, JD Stevenson, ...
Physical Chemistry Chemical Physics 19 (20), 12585-12603, 2017
Explorations on high dimensional landscapes
L Sagun, VU Guney, GB Arous, Y LeCun
arXiv preprint arXiv:1412.6615, 2014
Comparing dynamics: Deep neural networks versus glassy systems
M Baity-Jesi, L Sagun, M Geiger, S Spigler, GB Arous, C Cammarota, ...
Journal of Statistical Mechanics: Theory and Experiment 2019 (12), 124013, 2019
Jamming transition as a paradigm to understand the loss landscape of deep neural networks
M Geiger, S Spigler, S d'Ascoli, L Sagun, M Baity-Jesi, G Biroli, M Wyart
Physical Review E 100 (1), 012115, 2019
A jamming transition from under-to over-parametrization affects generalization in deep learning
S Spigler, M Geiger, S d’Ascoli, L Sagun, G Biroli, M Wyart
Journal of Physics A: Mathematical and Theoretical 52 (47), 474001, 2019
Scaling description of generalization with number of parameters in deep learning
M Geiger, A Jacot, S Spigler, F Gabriel, L Sagun, S d’Ascoli, G Biroli, ...
Journal of Statistical Mechanics: Theory and Experiment 2020 (2), 023401, 2020
A tail-index analysis of stochastic gradient noise in deep neural networks
U Simsekli, L Sagun, M Gurbuzbalaban
arXiv preprint arXiv:1901.06053, 2019
Early Predictability of Asylum Court Decisions
M Dunn, H Sirin, L Sagun, D Chen
Universal halting times in optimization and machine learning
L Sagun, T Trogdon, Y LeCun
arXiv preprint arXiv:1511.06444, 2015
Easing non-convex optimization with neural networks
D Lopez-Paz, L Sagun
Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
S d'Ascoli, L Sagun, G Biroli, J Bruna
Advances in Neural Information Processing Systems, 9330-9340, 2019
On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks
U Şimşekli, M Gürbüzbalaban, TH Nguyen, G Richard, L Sagun
arXiv preprint arXiv:1912.00018, 2019
Explorations on High Dimensional Landscapes: Spin Glasses and Deep Learning
L Sagun
New York University, 2017
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