777 citations · 1.8k across the 46 of their papers we have counts for
23 papers · 1 filter
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 Symmetric Perceptron: a Teacher-Student Scenario
Giovanni Catania, Aurélien Decelle, Suhanee Korpe
We introduce and solve a teacher-student formulation of the symmetric binary Perceptron, turning a traditionally storage-oriented model into a planted inference problem with a guar…
On the role of non-linear latent features in bipartite generative neural networks
Tony Bonnaire, Giovanni Catania, Aurélien Decelle +1
We investigate the phase diagram and memory retrieval capabilities of bipartite energy-based neural networks, namely Restricted Boltzmann Machines (RBMs), as a function of the prio…
Inferring Higher-Order Couplings with Neural Networks
Aurélien Decelle, Alfonso de Jesús Navas Gómez, Beatriz Seoane
Maximum entropy methods, rooted in the inverse Ising/Potts problem from statistical physics, are widely used to model pairwise interactions in complex systems across disciplines su…
Inferring effective couplings with Restricted Boltzmann Machines
Aurélien Decelle, Cyril Furtlehner, Alfonso De Jesus Navas Gómez +1
Generative models offer a direct way of modeling complex data. Energy-based models attempt to encode the statistical correlations observed in the data at the level of the Boltzmann…
The Copycat Perceptron: Smashing Barriers Through Collective Learning
Giovanni Catania, Aurélien Decelle, Beatriz Seoane
We characterize the equilibrium properties of a model of coupled binary perceptrons in the teacher-student scenario, subject to a suitable cost function, with an explicit ferro…