3 citations · 4 across the 3 of their papers we have counts for
3 papers
math.ST2025
Information-theoretic reduction of deep neural networks to linear models in the overparametrized proportional regime
Francesco Camilli, Daria Tieplova, Eleonora Bergamin +1
We rigorously analyse fully-trained neural networks of arbitrary depth in the Bayesian optimal setting in the so-called proportional scaling regime where the number of training sam…
cond-mat.dis-nn2023★ 1 cited
The Decimation Scheme for Symmetric Matrix Factorization
Francesco Camilli, Marc Mézard
Matrix factorization is an inference problem that has acquired importance due to its vast range of applications that go from dictionary learning to recommendation systems and machi…
cs.LG2023★ 3 cited
Fundamental limits of overparametrized shallow neural networks for supervised learning
Francesco Camilli, Daria Tieplova, Jean Barbier
We carry out an information-theoretical analysis of a two-layer neural network trained from input-output pairs generated by a teacher network with matching architecture, in overpar…