35 citations · 115 across the 10 of their papers we have counts for
6 papers · 1 filter
Random Features Hopfield Networks generalize retrieval to previously unseen examples
Silvio Kalaj, Clarissa Lauditi, Gabriele Perugini +3
It has been recently shown that a learning transition happens when a Hopfield Network stores examples generated as superpositions of random features, where new attractors correspon…
The star-shaped space of solutions of the spherical negative perceptron
Brandon Livio Annesi, Clarissa Lauditi, Carlo Lucibello +4
Empirical studies on the landscape of neural networks have shown that low-energy configurations are often found in complex connected structures, where zero-energy paths between pai…
Storage and Learning phase transitions in the Random-Features Hopfield Model
Matteo Negri, Clarissa Lauditi, Gabriele Perugini +2
The Hopfield model is a paradigmatic model of neural networks that has been analyzed for many decades in the statistical physics, neuroscience, and machine learning communities. In…
Typical and atypical solutions in non-convex neural networks with discrete and continuous weights
Carlo Baldassi, Enrico M. Malatesta, Gabriele Perugini +1
We study the binary and continuous negative-margin perceptrons as simple non-convex neural network models learning random rules and associations. We analyze the geometry of the lan…
Unveiling the structure of wide flat minima in neural networks
Carlo Baldassi, Clarissa Lauditi, Enrico M. Malatesta +2
The success of deep learning has revealed the application potential of neural networks across the sciences and opened up fundamental theoretical problems. In particular, the fact t…
An improved Belief Propagation algorithm finds many Bethe states in the random field Ising model on random graphs
Gabriele Perugini, Federico Ricci-Tersenghi
We first present an empirical study of the Belief Propagation (BP) algorithm, when run on the random field Ising model defined on random regular graphs in the zero temperature limi…