16 citations · 26 across the 9 of their papers we have counts for
10 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…
A Concept-based approach to Voice Disorder Detection
Davide Ghia, Gabriele Ciravegna, Alkis Koudounas +4
Voice disorders affect a significant portion of the population, and the ability to diagnose them using automated, non-invasive techniques would represent a substantial advancement…
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-…
MVP: Multi-source Voice Pathology detection
Alkis Koudounas, Moreno La Quatra, Gabriele Ciravegna +6
Voice disorders significantly impact patient quality of life, yet non-invasive automated diagnosis remains under-explored due to both the scarcity of pathological voice data, and t…
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…
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…
Voice Disorder Analysis: a Transformer-based Approach
Alkis Koudounas, Gabriele Ciravegna, Marco Fantini +4
Voice disorders are pathologies significantly affecting patient quality of life. However, non-invasive automated diagnosis of these pathologies is still under-explored, due to both…
Linearly-Interpretable Concept Embedding Models for Text Analysis
Francesco De Santis, Philippe Bich, Gabriele Ciravegna +3
Despite their success, Large-Language Models (LLMs) still face criticism due to their lack of interpretability. Traditional post-hoc interpretation methods, based on attention and…
Concept-based Explainable Artificial Intelligence: A Survey
Eleonora Poeta, Gabriele Ciravegna, Eliana Pastor +2
The field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models. However, using raw features to provide explanatio…