My name is Stefano Spigler. I am a postdoc in the Laboratory of Physics of Complex Systems at the École Polytechnique Fédérale de Lausanne (EPFL, Lausanne, Switzerland).
My current research focuses on the foundations of modern deep learning algorithms. In my most recent works I have studied
(i) how over-parametrization affects the loss landscape and the learning dynamics; (ii) how the generalization error of
kernel methods scales with the size of the training set; (iii) the limit of large hidden layers in deep networks.
Summary of education & academic positions
Download the full CV (pdf)
2017-2020
Postdoc
Eneergy landscape and learning dynamics in deep learning
In collaboration with:Matthieu Wyart & Giulio Biroli
Physics of Complex Systems Laboratory
École Polytechnique Fédérale de Lausanne
Eneergy landscape and learning dynamics in deep learning
In collaboration with:
École Polytechnique Fédérale de Lausanne
2014-2017
PhD in Physics
Distribution of avalanches in disordered systems
Supervisor:Silvio Franz
Laboratoire de Physique Théorique et Modèles Statistiques
Université Paris Sud (Université Paris-Saclay)
Scholarship by the École Normale Supérieure
You can read here my Ph.D. thesis
Distribution of avalanches in disordered systems
Supervisor:
Université Paris Sud (Université Paris-Saclay)
Scholarship by the École Normale Supérieure
2012-2014
Master in Physics of Complex Systems (link)
Politecnico di Torino ,
International School for Advanced Studies ,
International Centre for Theoretical Physics ,
Université Pierre et Marie Curie ,
Université Paris Diderot ,
Université Paris Sud ,
École Normale Supérieure de Cachan
Ranked 1st among all the participants
You can read here my M.Sc. thesis done at the Laboratoire de Physique Théorique et Modèles Statistiques under the supervision of Silvio Franz
Ranked 1st among all the participants
2009-2012
Scuola Galileiana di Studi Superiori (link) (scholarship)
Skills
Languages
Italian
English
French
Swedish (beginner)
Informatics
Debian/Red Hat based linux distros
C/C++, Python and Pytorch
XML and derived languages (HTML), CSS, JavaScript (basics), PHP
SQL (basics), Pandas, R (basics)
Mathematica (basics), LaTeX, Office suite
Publications
2019
M. Geiger, S. Spigler, A. Jacot, M. Wyart
Disentangling feature and lazy learning in deep neural networks: an empirical study
submitted (arXiv preprint)
Disentangling feature and lazy learning in deep neural networks: an empirical study
submitted (arXiv preprint)
2019
S. Spigler, M. Geiger, M. Wyart
Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm
submitted (arXiv preprint)
Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm
submitted (arXiv preprint)
2019
M. Geiger, A. Jacot, S. Spigler, F. Gabriel, L. Sagun, S. d'Ascoli, G. Biroli, C. Hongler, M. Wyart
Scaling description of generalization with number of parameters in deep learning
submitted (arXiv preprint)
Scaling description of generalization with number of parameters in deep learning
submitted (arXiv preprint)
2018
S. Spigler, M. Geiger, S. d'Ascoli, L. Sagun, M. Baity-Jesi, G. Biroli, M. Wyart
A jamming transition from under- to over-parametrization affects loss landscape and generalization
NeurIPS 2018 workshop "Integration of Deep Learning Theories" (arXiv preprint)
A jamming transition from under- to over-parametrization affects loss landscape and generalization
NeurIPS 2018 workshop "Integration of Deep Learning Theories" (arXiv preprint)
2018
M. Geiger, S. Spigler, S. d'Ascoli, L. Sagun, M. Baity-Jesi, G. Biroli, M. Wyart
The jamming transition as a paradigm to understand the loss landscape of deep neural networks
Phys. Rev. E 100, 012115 (PR E)
The jamming transition as a paradigm to understand the loss landscape of deep neural networks
Phys. Rev. E 100, 012115 (PR E)
2018
M. Baity-Jesi, L. Sagun, M. Geiger, S. Spigler, G.B. Arous, C. Cammarota, Y. LeCun, M. Wyart, G. Biroli
Comparing dynamics: deep neural networks versus glassy systems
ICML, PMLR 80:314-323 (PMLR)
Comparing dynamics: deep neural networks versus glassy systems
ICML, PMLR 80:314-323 (PMLR)
2016
S. Franz, G. Gradenigo, S. Spigler
Random-diluted triangular plaquette model: Study of phase transitions in a kinetically constrained model
Phys. Rev. E 93(3), 032601 (PR E)
Random-diluted triangular plaquette model: Study of phase transitions in a kinetically constrained model
Phys. Rev. E 93(3), 032601 (PR E)