2 papers
cs.LG2026
Challenges of Explainability in Continual Learning for Time Series Forecasting
Quentin Besnard, Emmanuel Doumard, Nicolas Labroche +2
Deep learning models have shown strong potential for time series forecasting, yet their deployment in real-world environmental monitoring remains challenging due to non-stationary…
cs.LG2023
L'explicabilité au service de l'extraction de connaissances : application à des données médicales
Robin Cugny, Emmanuel Doumard, Elodie Escriva +1
The use of machine learning has increased dramatically in the last decade. The lack of transparency is now a limiting factor, which the field of explainability wants to address. Fu…