3 papers
cs.LG2026
Are Current Continual Learning Methods Truly Agnostic? Introducing OPRE, a Step Toward Agnostic Continual Learning
Raphaël Bayle, Martial Mermillod, Robert M. French
In order to achieve Continual Learning (CL), the problem of catastrophic forgetting, one that has plagued neural networks since their inception, must be overcome. The evaluation of…
cs.LG2026
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting
Nouha Karaouli, Denis Coquenet, Elisa Fromont +2
While Time Series Foundation Models (TSFMs) excel in zero-shot tasks, their behavior under continual fine tuning is poorly understood. We present the first systematic study of cata…
cs.LG2025
How Foundational are Foundation Models for Time Series Forecasting?
Nouha Karaouli, Denis Coquenet, Elisa Fromont +2
Foundation Models are designed to serve as versatile embedding machines, with strong zero shot capabilities and superior generalization performance when fine-tuned on diverse downs…