Random features and polynomial rules

no code implementations • 15 Feb 2024 • Fabián Aguirre-López, Silvio Franz, Mauro Pastore

Random features models play a distinguished role in the theory of deep learning, describing the behavior of neural networks close to their infinite-width limit.

Surfing on minima of isostatic landscapes: avalanches and unjamming transition

no code implementations • 5 Oct 2020 • Silvio Franz, Antonio Sclocchi, Pierfrancesco Urbani

This algorithm allows to "surf" between isostatic marginally stable configurations and to investigate some properties of such landscape.

Disordered Systems and Neural Networks Statistical Mechanics

A fast and accurate algorithm for inferring sparse Ising models via parameters activation to maximize the pseudo-likelihood

1 code implementation • 31 Jan 2019 • Silvio Franz, Federico Ricci-Tersenghi, Jacopo Rocchi

We propose a new algorithm to learn the network of the interactions of pairwise Ising models.

Disordered Systems and Neural Networks

Glassy nature of the hard phase in inference problems

no code implementations • 15 May 2018 • Fabrizio Antenucci, Silvio Franz, Pierfrancesco Urbani, Lenka Zdeborová

An algorithmically hard phase was described in a range of inference problems: even if the signal can be reconstructed with a small error from an information theoretic point of view, known algorithms fail unless the noise-to-signal ratio is sufficiently small.