4 papers
On the Batch Size Selection in Stochastic Gradient Methods Using No-Replacement Sampling
Marco Boresta, Alberto De Santis, Stefano Lucidi
Recent stochastic gradient methods that have appeared in the literature base their efficiency and global convergence properties on a suitable control of the variance of the gradien…
Computational issues in Optimization for Deep networks
Corrado Coppola, Lorenzo Papa, Marco Boresta +2
The paper aims to investigate relevant computational issues of deep neural network architectures with an eye to the interaction between the optimization algorithm and the classific…
Convolutional neural networks and multi-threshold analysis for contamination detection in the apparel industry
Marco Boresta, Tommaso Colombo, Alberto De Santis
Quality control of apparel items is mandatory in modern textile industry, as consumer's awareness and expectations about the highest possible standard is constantly increasing in f…
Mixed Finite Differences Scheme for Gradient Approximation
Marco Boresta, Tommaso Colombo, Alberto De Santis +1
In this paper we focus on the linear functionals defining an approximate version of the gradient of a function. These functionals are often used when dealing with optimization prob…