13 citations · 20 across the 3 of their papers we have counts for
10 papers · 1 filter
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…
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…
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 Graph Networks Formulate Universal Algebra Conjectures
Francesco Giannini, Stefano Fioravanti, Oguzhan Keskin +4
The rise of Artificial Intelligence (AI) recently empowered researchers to investigate hard mathematical problems which eluded traditional approaches for decades. Yet, the use of A…
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…
On the relation between Loss Functions and T-Norms
Francesco Giannini, Giuseppe Marra, Michelangelo Diligenti +2
Deep learning has been shown to achieve impressive results in several domains like computer vision and natural language processing. A key element of this success has been the devel…