Stefano Spigler
Title
Cited by
Cited by
Year
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
56*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
562019
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
562018
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
522020
Mean-field avalanches in jammed spheres
S Franz, S Spigler
Physical Review E 95 (2), 022139, 2017
292017
Disentangling feature and lazy learning in deep neural networks: an empirical study.
M Geiger, S Spigler, A Jacot, M Wyart
19*2019
Asymptotic learning curves of kernel methods: empirical data vs Teacher-Student paradigm
S Spigler, M Geiger, M Wyart
arXiv preprint arXiv:1905.10843, 2019
72019
Random-diluted triangular plaquette model: Study of phase transitions in a kinetically constrained model
S Franz, G Gradenigo, S Spigler
Physical Review E 93 (3), 032601, 2016
22016
How isotropic kernels learn simple invariants
J Paccolat, S Spigler, M Wyart
arXiv preprint arXiv:2006.09754, 2020
2020
Les avalanches dans les systèmes vitreux
S Spigler
2017
Avalanches in glassy system
S Spigler
Université Paris Sud, Université Paris Saclay, 2017
2017
Plaquette models for glasses
S Spigler
2014
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Articles 1–12