4 citations · 5 across the 6 of their papers we have counts for
7 papers
Information-Theoretic Limits for Rectangular Spiked Matrix Models with Rotationally Invariant Noise
Qun Chen, Rudy Skerk, Jean Barbier +3
We study the information-theoretic limits of inference in rectangular rank-one spiked matrix models with additive rotationally invariant noise, thereby going beyond the standard as…
Statistical physics of deep learning: Optimal learning of a multi-layer perceptron near interpolation
Jean Barbier, Francesco Camilli, Minh-Toan Nguyen +2
For four decades statistical physics has been providing a framework to analyse neural networks. A long-standing question remained on its capacity to tackle deep learning models cap…
Statistical mechanics of extensive-width Bayesian neural networks near interpolation
Jean Barbier, Francesco Camilli, Minh-Toan Nguyen +2
For three decades statistical mechanics has been providing a framework to analyse neural networks. However, the theoretically tractable models, e.g., perceptrons, random features m…
Optimal generalisation and learning transition in extensive-width shallow neural networks near interpolation
Jean Barbier, Francesco Camilli, Minh-Toan Nguyen +2
We consider a teacher-student model of supervised learning with a fully-trained two-layer neural network whose width and input dimension are large and proportional. We prov…
On the phase diagram of extensive-rank symmetric matrix denoising beyond rotational invariance
Jean Barbier, Francesco Camilli, Justin Ko +1
Matrix denoising is central to signal processing and machine learning. Its statistical analysis when the matrix to infer has a factorised structure with a rank growing proportional…
Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noise
Jean Barbier, Francesco Camilli, Marco Mondelli +1
We consider a prototypical problem of Bayesian inference for a structured spiked model: a low-rank signal is corrupted by additive noise. While both information-theoretic and algor…
A multiscale cavity method for sublinear-rank symmetric matrix factorization
Jean Barbier, Justin Ko, Anas A. Rahman
We consider a statistical model for symmetric matrix factorization with additive Gaussian noise in the high-dimensional regime, where the rank of the signal matrix to infer sca…