3 papers
cs.LG2026
Interpretability in Deep Time Series Models Demands Semantic Alignment
Giovanni De Felice, Riccardo D'Elia, Alberto Termine +3
Deep time series models continue to improve predictive performance, yet their deployment remains limited by their black-box nature. In response, existing interpretability approache…
cs.LG2026
A foundation model for electrodermal activity data
Leonardo Alchieri, Matteo Garzon, Lidia Alecci +4
Foundation models have recently extended beyond natural language and vision to timeseries domains, including physiological signals. However, progress in electrodermal activity (EDA…
cs.LG2026
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…