29 citations · 33 across the 6 of their papers we have counts for
7 papers
Algorithmic Concept-based Explainable Reasoning
Dobrik Georgiev, Pietro Barbiero, Dmitry Kazhdan +2
Recent research on graph neural network (GNN) models successfully applied GNNs to classical graph algorithms and combinatorial optimisation problems. This has numerous benefits, su…
PyTorch, Explain! A Python library for Logic Explained Networks
Pietro Barbiero, Gabriele Ciravegna, Dobrik Georgiev +1
"PyTorch, Explain!" is a Python module integrating a variety of state-of-the-art approaches to provide logic explanations from neural networks. This package focuses on bringing the…
Graph representation forecasting of patient's medical conditions: towards a digital twin
Pietro Barbiero, Ramon Viñas Torné, Pietro Lió
Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalised, systemic and pre…
Gradient-based Competitive Learning: Theory
Giansalvo Cirrincione, Pietro Barbiero, Gabriele Ciravegna +1
Deep learning has been widely used for supervised learning and classification/regression problems. Recently, a novel area of research has applied this paradigm to unsupervised task…
The Computational Patient has Diabetes and a COVID
Pietro Barbiero, Pietro Lió
Medicine is moving from a curative discipline to a preventative discipline relying on personalised and precise treatment plans. The complex and multi level pathophysiological patte…
Modeling Generalization in Machine Learning: A Methodological and Computational Study
Pietro Barbiero, Giovanni Squillero, Alberto Tonda
As machine learning becomes more and more available to the general public, theoretical questions are turning into pressing practical issues. Possibly, one of the most relevant conc…