8 citations · 15 across the 25 of their papers we have counts for
5 papers · 1 filter
ATE-SG: Alternate Through the Epochs Stochastic Gradient for Multi-Task Neural Networks
Stefania Bellavia, Francesco Della Santa, Alessandra Papini
This paper introduces novel alternate training procedures for hard-parameter sharing Multi-Task Neural Networks (MTNNs). Traditional MTNN training faces challenges in managing conf…
An optimally fast objective-function-free minimization algorithm using random subspaces
S. Bellavia, S. Gratton, B. Morini +1
An algorithm for unconstrained non-convex optimization is described, which does not evaluate the objective function and in which minimization is carried out, at each iteration, wit…
Inexact Gauss-Newton methods with matrix approximation by sampling for nonlinear least-squares and systems
Stefania Bellavia, Greta Malaspina, Benedetta Morini
We develop and analyze stochastic inexact Gauss-Newton methods for nonlinear least-squares problems and for nonlinear systems ofequations. Random models are formed using suitable s…
Regularized methods via cubic model subspace minimization for nonconvex optimization
Stefania Bellavia, Davide Palitta, Margherita Porcelli +1
Adaptive cubic regularization methods for solving nonconvex problems need the efficient computation of the trial step, involving the minimization of a cubic model. We propose a new…
SLiSeS: Subsampled Line Search Spectral Gradient Method for Finite Sums
Stefania Bellavia, Nataša Krejić, Nataša Krklec Jerinkić +1
The spectral gradient method is known to be a powerful low-cost tool for solving large-scale optimization problems. In this paper, our goal is to exploit its advantages in the stoc…