60 citations · 235 across the 23 of their papers we have counts for
23 papers
Generalization performance of narrow one-hidden layer networks in the teacher-student setting
Rodrigo Pérez Ortiz, Gibbs Nwemadji, Jean Barbier +4
Understanding the generalization properties of neural networks on simple input-output distributions is key to explaining their performance on real datasets. The classical teacher-s…
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…
Instantons in Theories: Transseries, Virial Theorems and Numerical Aspects
Ludovico T. Giorgini, Ulrich D. Jentschura, Enrico M. Malatesta +2
We discuss numerical aspects of instantons in two- and three-dimensional theories with an internal symmetry group, the so-called -vector model. Combining asymptotic…
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…