8 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…
Federated Concept-Based Models: Interpretable models with distributed supervision
Dario Fenoglio, Arianna Casanova, Francesco De Santis +6
Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept annotations are costly and rarely a…
Incremental learning for audio classification with Hebbian Deep Neural Networks
Riccardo Casciotti, Francesco De Santis, Alberto Antonietti +1
The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we apply Hebbian learning, a biologi…
Causally Reliable Concept Bottleneck Models
Giovanni De Felice, Arianna Casanova Flores, Francesco De Santis +4
Concept-based models are an emerging paradigm in deep learning that constrains the inference process to operate through human-interpretable variables, facilitating explainability a…
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…
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-…