Levent Sagun
Levent Sagun
Verifisert e-postadresse på meta.com
Sitert av
Sitert av
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
Convit: Improving vision transformers with soft convolutional inductive biases
S d’Ascoli, H Touvron, ML Leavitt, AS Morcos, G Biroli, L Sagun
International conference on machine learning, 2286-2296, 2021
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
A tail-index analysis of stochastic gradient noise in deep neural networks
U Simsekli, L Sagun, M Gurbuzbalaban
International Conference on Machine Learning, 5827-5837, 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 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
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
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
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
Vision models are more robust and fair when pretrained on uncurated images without supervision
P Goyal, Q Duval, I Seessel, M Caron, I Misra, L Sagun, A Joulin, ...
arXiv preprint arXiv:2202.08360, 2022
Triple descent and the two kinds of overfitting: Where & why do they appear?
S d'Ascoli, L Sagun, G Biroli
Advances in neural information processing systems 33, 3058-3069, 2020
Explorations on high dimensional landscapes
L Sagun, VU Guney, GB Arous, Y LeCun
arXiv preprint arXiv:1412.6615, 2014
Early Predictability of Asylum Court Decisions
M Dunn, H Sirin, L Sagun, D Chen
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 32, 2019
Fairness indicators for systematic assessments of visual feature extractors
P Goyal, AR Soriano, C Hazirbas, L Sagun, N Usunier
Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022
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
On the interplay between data structure and loss function in classification problems
S d'Ascoli, M Gabrié, L Sagun, G Biroli
Advances in Neural Information Processing Systems 34, 8506-8517, 2021
Easing non-convex optimization with neural networks
D Lopez-Paz, L Sagun
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