65 citations · 107 across the 16 of their papers we have counts for
16 papers
Interpretable Concept-Based Memory Reasoning
David Debot, Pietro Barbiero, Francesco Giannini +3
The lack of transparency in the decision-making processes of deep learning systems presents a significant challenge in modern artificial intelligence (AI), as it impairs users' abi…
Linearly-Interpretable Concept Embedding Models for Text Analysis
Francesco De Santis, Philippe Bich, Gabriele Ciravegna +3
Despite their success, Large-Language Models (LLMs) still face criticism due to their lack of interpretability. Traditional post-hoc interpretation methods, based on attention and…
AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model
Gabriele Dominici, Pietro Barbiero, Francesco Giannini +2
Interpretable deep learning aims at developing neural architectures whose decision-making processes could be understood by their users. Among these techniqes, Concept Bottleneck Mo…
Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
Gabriele Dominici, Pietro Barbiero, Mateo Espinosa Zarlenga +4
Causal opacity denotes the difficulty in understanding the "hidden" causal structure underlying the decisions of deep neural network (DNN) models. This leads to the inability to re…
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
Dario Fenoglio, Gabriele Dominici, Pietro Barbiero +3
Federated Learning (FL), a privacy-aware approach in distributed deep learning environments, enables many clients to collaboratively train a model without sharing sensitive data, t…
Counterfactual Concept Bottleneck Models
Gabriele Dominici, Pietro Barbiero, Francesco Giannini +3
Current deep learning models are not designed to simultaneously address three fundamental questions: predict class labels to solve a given classification task (the "What?"), simula…
Categorical Foundations of Explainable AI: A Unifying Theory
Pietro Barbiero, Stefano Fioravanti, Francesco Giannini +3
Explainable AI (XAI) aims to address the human need for safe and reliable AI systems. However, numerous surveys emphasize the absence of a sound mathematical formalization of key X…
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…
Entropy-based Logic Explanations of Neural Networks
Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini +3
Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have…
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…
Topological Gradient-based Competitive Learning
Pietro Barbiero, Gabriele Ciravegna, Vincenzo Randazzo +1
Topological learning is a wide research area aiming at uncovering the mutual spatial relationships between the elements of a set. Some of the most common and oldest approaches invo…
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…
Uncovering Coresets for Classification With Multi-Objective Evolutionary Algorithms
Pietro Barbiero, Giovanni Squillero, Alberto Tonda
A coreset is a subset of the training set, using which a machine learning algorithm obtains performances similar to what it would deliver if trained over the whole original data. C…