166 citations · 508 across the 8 of their papers we have counts for
3 papers · 1 filter
A jamming transition from under- to over-parametrization affects loss landscape and generalization
Stefano Spigler, Mario Geiger, Stéphane d'Ascoli +3
We argue that in fully-connected networks a phase transition delimits the over- and under-parametrized regimes where fitting can or cannot be achieved. Under some general condition…
The jamming transition as a paradigm to understand the loss landscape of deep neural networks
Mario Geiger, Stefano Spigler, Stéphane d'Ascoli +4
Deep learning has been immensely successful at a variety of tasks, ranging from classification to AI. Learning corresponds to fitting training data, which is implemented by descend…
Comparing Dynamics: Deep Neural Networks versus Glassy Systems
M. Baity-Jesi, L. Sagun, M. Geiger +6
We analyze numerically the training dynamics of deep neural networks (DNN) by using methods developed in statistical physics of glassy systems. The two main issues we address are (…