2 citations
4 papers
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…
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…
Nonzero RMS Magnetoresistance Yielding Control Space Partition of CrTe2 Monolayer
Chee Kian Yap, Arun Kumar Singh
The study of magnetic phenomena in low-dimensional systems has largely explored after the discovery of two-dimensional (2D) magnetic materials, such as CrI3 and Cr2Ge2Te6 in 2017.…
Asymmetric nuclear matter : a variational approach
S. Sarangi, P. K. Panda, S. K. Sahu +1
We discuss here a self-consistent method to calculate the properties of the cold asymmetric nuclear matter. In this model, the nuclear matter is dressed with s-wave pion pairs and…