Levent Sagun
Title
Cited by
Cited by
Year
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
3032016
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
1522017
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
1252017
Eigenvalues of the hessian in deep learning: Singularity and beyond
L Sagun, L Bottou, Y LeCun
arXiv preprint arXiv:1611.07476, 2016
84*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
622017
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
532019
Comparing dynamics: Deep neural networks versus glassy systems
M Baity-Jesi, L Sagun, M Geiger, S Spigler, GB Arous, C Cammarota, ...
International Conference on Machine Learning, 314-323, 2018
522018
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
50*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
452020
Explorations on high dimensional landscapes
L Sagun, VU Guney, GB Arous, Y LeCun
arXiv preprint arXiv:1412.6615, 2014
452014
A tail-index analysis of stochastic gradient noise in deep neural networks
U Simsekli, L Sagun, M Gurbuzbalaban
arXiv preprint arXiv:1901.06053, 2019
442019
Early Predictability of Asylum Court Decisions
M Dunn, H Sirin, L Sagun, D Chen
22*2017
Universal halting times in optimization and machine learning
L Sagun, T Trogdon, Y LeCun
arXiv preprint arXiv:1511.06444, 2015
9*2015
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, 9334-9345, 2019
52019
Easing non-convex optimization with neural networks
D Lopez-Paz, L Sagun
32018
Triple descent and the two kinds of overfitting: Where & why do they appear?
S d'Ascoli, L Sagun, G Biroli
arXiv preprint arXiv:2006.03509, 2020
22020
Post-Workshop Report on, Science meets Engineering in Deep Learning, NeurIPS 2019, Vancouver
L Sagun, C Gulcehre, A Romero, N Rostemzadeh, SS Mannelli
arXiv preprint arXiv:2007.13483, 2020
2020
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
2019
5TH INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS, ICLR 2017-CONFERENCE TRACK PROCEEDINGS
Y Yang, TM Hospedales, J Park, S Li, PTP Tang, P Dubey, W Wen, H Li, ...
2019
Explorations on High Dimensional Landscapes: Spin Glasses and Deep Learning
L Sagun
New York University, 2017
2017
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Articles 1–20