11 citations · 18 across the 4 of their papers we have counts for
4 papers
Neural Renormalization Group Flow for Percolation
Anaclara Alvez, Luca Camagna, Sergio Chibbaro +4
Machine learning offers a possible route to data-driven real-space renormalization when the relevant observables are nonlocal and difficult to prescribe explicitly. We explore this…
A solvable model for unsupervised federated learning
Giovanni Catania, Aurélien Decelle, Gianluca Manzan +2
We introduce a theoretical framework for analyzing federated learning in a generative setting through a teacher-multiple interacting students scenario, in which each student receiv…
The effect of priors on Learning with Restricted Boltzmann Machines
Gianluca Manzan, Daniele Tantari
Restricted Boltzmann Machines (RBMs) are generative models designed to learn from data with a rich underlying structure. In this work, we explore a teacher-student setting where a…
Hopfield model with planted patterns: a teacher-student self-supervised learning model
Francesco Alemanno, Luca Camanzi, Gianluca Manzan +1
While Hopfield networks are known as paradigmatic models for memory storage and retrieval, modern artificial intelligence systems mainly stand on the machine learning paradigm. We…