221 citations · 471 across the 24 of their papers we have counts for
4 papers · 1 filter
LOss-Based SensiTivity rEgulaRization: towards deep sparse neural networks
Enzo Tartaglione, Andrea Bragagnolo, Attilio Fiandrotti +1
LOBSTER (LOss-Based SensiTivity rEgulaRization) is a method for training neural networks having a sparse topology. Let the sensitivity of a network parameter be the variation of th…
A non-discriminatory approach to ethical deep learning
Enzo Tartaglione, Marco Grangetto
Artificial neural networks perform state-of-the-art in an ever-growing number of tasks, nowadays they are used to solve an incredibly large variety of tasks. However, typical train…
Pruning artificial neural networks: a way to find well-generalizing, high-entropy sharp minima
Enzo Tartaglione, Andrea Bragagnolo, Marco Grangetto
Recently, a race towards the simplification of deep networks has begun, showing that it is effectively possible to reduce the size of these models with minimal or no performance lo…
Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
Enzo Tartaglione, Carlo Alberto Barbano, Claudio Berzovini +2
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI commun…