Comparing Dynamics: Deep Neural Networks versus Glassy Systems. - Marco Baity-Jesi; Levent Sagun; Mario Geiger; Stefano Spigler; Gérard Ben Arous; Chiara Cammarota; Yann LeCun; Matthieu Wyart; Giulio Biroli (ICML, 2018)

The jamming transition as a paradigm to understand the loss landscape of deep neural networks. - Mario Geiger; Stefano Spigler; Stéphane d'Ascoli; Levent Sagun; Marco Baity-Jesi; Giulio Biroli; Matthieu Wyart (CoRR, 2018)

A jamming transition from under- to over-parametrization affects loss landscape and generalization. - Stefano Spigler; Mario Geiger; Stéphane d'Ascoli; Levent Sagun; Giulio Biroli; Matthieu Wyart (CoRR, 2018)

Scaling description of generalization with number of parameters in deep learning. - Mario Geiger; Arthur Jacot; Stefano Spigler; Franck Gabriel; Levent Sagun; Stéphane d'Ascoli; Giulio Biroli; Clément Hongler; Matthieu Wyart (CoRR, 2019)

Asymptotic learning curves of kernel methods: empirical data v.s. Teacher-Student paradigm. - Stefano Spigler; Mario Geiger; Matthieu Wyart (CoRR, 2019)

Disentangling feature and lazy learning in deep neural networks: an empirical study. - Mario Geiger; Stefano Spigler; Arthur Jacot; Matthieu Wyart (CoRR, 2019)

How isotropic kernels learn simple invariants. - Jonas Paccolat; Stefano Spigler; Matthieu Wyart (CoRR, 2020)

How isotropic kernels perform on simple invariants. - Jonas Paccolat; Stefano Spigler; Matthieu Wyart (Mach. Learn. Sci. Technol., 2021)