3 papers
cs.LG2026
Is Stochastic Gradient Descent Effective? A PDE Perspective on Machine Learning processes
Davide Barbieri, Matteo Bonforte, Peio Ibarrondo
In this paper we analyze the behaviour of the stochastic gradient descent (SGD), a widely used method in supervised learning for optimizing neural network weights via a minimizatio…
math.FA2026
Sampling in the Euclidean Motion Group and a Problem from Brain's Primary Visual Cortex
Davide Barbieri
We study a sampling problem for the abstract wavelet transform associated with the quasiregular representation of the group, for a modulated gaussian mother wavelet. This p…
math.NA2025
Scattering Networks on Noncommutative Finite Groups
Maria Teresa Arias, Davide Barbieri, Eugenio Hernández
Scattering Networks were initially designed to elucidate the behavior of early layers in Convolutional Neural Networks (CNNs) over Euclidean spaces and are grounded in wavelets. In…