78 citations · 262 across the 18 of their papers we have counts for
21 papers
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…
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…
Enhancing Embedding Representations of Biomedical Data using Logic Knowledge
Michelangelo Diligenti, Francesco Giannini, Stefano Fioravanti +3
Knowledge Graph Embeddings (KGE) have become a quite popular class of models specifically devised to deal with ontologies and graph structure data, as they can implicitly encode st…