78 citations · 257 across the 13 of their papers we have counts for
14 papers
Measuring What Matters: A Unified Evaluation Framework for GNN Explainability
Francesco Paolo Nerini, Mirko Zaffaroni, Paolo Baracco +2
Graph eXplainable AI (G-XAI) is increasingly important for making Graph Neural Networks interpretable and accountable. While a growing number of explainers are available, choosing…
Relational Concept Bottleneck Models
Pietro Barbiero, Francesco Giannini, Gabriele Ciravegna +2
The design of interpretable deep learning models working in relational domains poses an open challenge: interpretable deep learning methods, such as Concept Bottleneck Models (CBMs…
Interpretable Neural-Symbolic Concept Reasoning
Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini +7
Deep learning methods are highly accurate, yet their opaque decision process prevents them from earning full human trust. Concept-based models aim to address this issue by learning…
Extending Logic Explained Networks to Text Classification
Rishabh Jain, Gabriele Ciravegna, Pietro Barbiero +3
Recently, Logic Explained Networks (LENs) have been proposed as explainable-by-design neural models providing logic explanations for their predictions. However, these models have o…
Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off
Mateo Espinosa Zarlenga, Pietro Barbiero, Gabriele Ciravegna +9
Deploying AI-powered systems requires trustworthy models supporting effective human interactions, going beyond raw prediction accuracy. Concept bottleneck models promote trustworth…
Encoding Concepts in Graph Neural Networks
Lucie Charlotte Magister, Pietro Barbiero, Dmitry Kazhdan +5
The opaque reasoning of Graph Neural Networks induces a lack of human trust. Existing graph network explainers attempt to address this issue by providing post-hoc explanations, how…
Knowledge-driven Active Learning
Gabriele Ciravegna, Frédéric Precioso, Alessandro Betti +2
The deployment of Deep Learning (DL) models is still precluded in those contexts where the amount of supervised data is limited. To answer this issue, active learning strategies ai…
Graph Neural Networks for Graph Drawing
Matteo Tiezzi, Gabriele Ciravegna, Marco Gori
Graph Drawing techniques have been developed in the last few years with the purpose of producing aesthetically pleasing node-link layouts. Recently, the employment of differentiabl…
Logic Explained Networks
Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini +4
The large and still increasing popularity of deep learning clashes with a major limit of neural network architectures, that consists in their lack of capability in providing human-…
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…
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…
Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers
Stefano Melacci, Gabriele Ciravegna, Angelo Sotgiu +4
Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems. In this p…