36 citations · 115 across the 16 of their papers we have counts for
20 papers
Mixture of Concept Bottleneck Experts
Francesco De Santis, Gabriele Ciravegna, Giovanni De Felice +7
Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs typically constrain their task predictor…
DeepProofLog: Efficient Proving in Deep Stochastic Logic Programs
Ying Jiao, Rodrigo Castellano Ontiveros, Luc De Raedt +4
Neurosymbolic (NeSy) AI aims to combine the strengths of neural architectures and symbolic reasoning to improve the accuracy, interpretability, and generalization capability of AI…
Grounding Methods for Neural-Symbolic AI
Rodrigo Castellano Ontiveros, Francesco Giannini, Marco Gori +2
A large class of Neural-Symbolic (NeSy) methods employs a machine learner to process the input entities, while relying on a reasoner based on First-Order Logic to represent and pro…
Neural Interpretable Reasoning
Pietro Barbiero, Giuseppe Marra, Gabriele Ciravegna +5
We formalize a novel modeling framework for achieving interpretability in deep learning, anchored in the principle of inference equivariance. While the direct verification of inter…
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…
Explainable Malware Detection with Tailored Logic Explained Networks
Peter Anthony, Francesco Giannini, Michelangelo Diligenti +4
Malware detection is a constant challenge in cybersecurity due to the rapid development of new attack techniques. Traditional signature-based approaches struggle to keep pace with…