3 papers
cs.AI2025
Towards explainable decision support using hybrid neural models for logistic terminal automation
Riccardo D'Elia, Alberto Termine, Francesco Flammini
The integration of Deep Learning (DL) in System Dynamics (SD) modeling for transportation logistics offers significant advantages in scalability and predictive accuracy. However, t…
cs.LG2025
Foundations of Interpretable Models
Pietro Barbiero, Mateo Espinosa Zarlenga, Alberto Termine +2
We argue that existing definitions of interpretability are not actionable in that they fail to inform users about general, sound, and robust interpretable model design. This makes…
cs.LG2025
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…