3 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.AI2026
Attention-based Experience Replay Framework for Continual Learning of Agnostic Time Series Forecasting Models
Quentin Besnard, Nicolas Ragot
Deep learning has led to remarkable progress in artificial intelligence, particularly in robotics, imaging and sound processing. However, a major limitation of neural networks rema…
cs.CV2025
Deep semi-supervised approach based on consistency regularization and similarity learning for weeds classification
Farouq Benchallal, Adel Hafiane, Nicolas Ragot +1
Weed species classification represents an important step for the development of automated targeting systems that allow the adoption of precision agriculture practices. To reduce co…