Stefano Spigler

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Matthieu WyartProfessor of Physics, Johns HopkinsVerified email at epfl.ch
Mario GeigerMITVerified email at mit.edu
Levent SagunFAIRVerified email at meta.com
Giulio BiroliProfessor of Theoretical Physics, ENS ParisVerified email at cea.fr
Stéphane d'AscoliResearch Scientist at Meta AIVerified email at meta.com
Arthur JacotAssistant Professor, Courant Institute of Mathematical Sciences, NYUVerified email at nyu.edu
Marco Baity-JesiEawagVerified email at eawag.ch
Clément HonglerInstitute of Mathematics, EPFLVerified email at epfl.ch
Silvio FranzProfessor of Statistical Physics LPTMS Universite Paris-SaclayVerified email at u-psud.fr
Gerard BEN AROUSProfessor of Mathematics, NYU Shanghai and Courant Institute of Mathematical Sciences, New YorkVerified email at nyu.edu
Yann LeCunChief AI Scientist at Facebook & Silver Professor at the Courant Institute, New York UniversityVerified email at cs.nyu.edu
Chiara CammarotaLa Sapienza, University of RomeVerified email at uniroma1.it
Franck GabrielUniversity Lyon 1, ISFA

Stefano Spigler

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Statistical Physics & AI


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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	231	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	223*	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	176	2019
Disentangling feature and lazy learning in deep neural networks: an empirical study M Geiger, S Spigler, A Jacot, M Wyart arXiv preprint arXiv:1906.08034, 2019	165*	2019
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	127	2018
Asymptotic learning curves of kernel methods: empirical data versus teacher–student paradigm S Spigler, M Geiger, M Wyart Journal of Statistical Mechanics: Theory and Experiment 2020 (12), 124001, 2020	93	2020
Mean-field avalanches in jammed spheres S Franz, S Spigler Physical Review E 95 (2), 022139, 2017	52	2017
How isotropic kernels perform on simple invariants J Paccolat, S Spigler, M Wyart Machine Learning: Science and Technology 2 (2), 025020, 2021	11*	2021
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	6	2016
Les avalanches dans les systèmes vitreux\| Theses. fr S Spigler Paris Sciences et Lettres (ComUE), 2017		2017
Les avalanches dans les systèmes vitreux S Spigler Université Paris sciences et lettres, 2017		2017
Avalanches in glassy system S Spigler Université Paris Sud, Université Paris Saclay, 2017		2017
Plaquette models for glasses S Spigler		2014
PCSL M Battilana, B Bravi, C Burkhard, F Cagnetta, AM De Domenico, ...

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