16 citations · 26 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
Towards Better Generalization and Interpretability in Unsupervised Concept-Based Models
Francesco De Santis, Philippe Bich, Gabriele Ciravegna +3
To increase the trustworthiness of deep neural networks, it is critical to improve the understanding of how they make decisions. This paper introduces a novel unsupervised concept-…
V-CEM: Bridging Performance and Intervenability in Concept-based Models
Francesco De Santis, Gabriele Ciravegna, Philippe Bich +2
Concept-based eXplainable AI (C-XAI) is a rapidly growing research field that enhances AI model interpretability by leveraging intermediate, human-understandable concepts. This app…
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…