Dear all,

On Monday, November 24th, at 14h00 in Aula Dal Passo of Tor Vergata Math Department, RoMaDS (https://www.mat.uniroma2.it/~rds/events.php) will host

Luisa Andreis (Università degli studi di Torino) with the seminar

"Large deviations for the covariance process in fully connected Gaussian neural networks”

Abstract: In this talk, we study fully connected Gaussian deep neural networks, focusing on their covariance process. In particular, we establish a large deviation principle (LDP) for this process in a functional framework, viewing it as a trajectory in the space of continuous functions. As key applications of our main results, we derive posterior LDPs under Gaussian likelihoods in both the infinite-width and mean-field regimes. We will outline the main ideas of the proof, emphasizing that it relies on an LDP for the covariance process regarded as a Markov process taking values in the space of non-negative, symmetric, trace-class operators equipped with the trace norm.
This talk is based on joint work with F. Bassetti and C. Hirsch.

We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming: