4 papers
UTICA: Multi-Objective Self-Distllation Foundation Model Pretraining for Time Series Classification
Yessin Moakher, Youssef Attia El Hili, Vasilii Feofanov
Self-supervised foundation models have achieved remarkable success across domains, including time series. However, the potential of non-contrastive methods, a paradigm that has dri…
Generalization in Representation Models via Random Matrix Theory: Application to Recurrent Networks
Yessin Moakher, Malik Tiomoko, Cosme Louart +1
We first study the generalization error of models that use a fixed feature representation (frozen intermediate layers) followed by a trainable readout layer. This setting encompass…
Leveraging Generic Time Series Foundation Models for EEG Classification
Théo Gnassounou, Yessin Moakher, Shifeng Xie +2
Foundation models for time series are emerging as powerful general-purpose backbones, yet their potential for domain-specific biomedical signals such as electroencephalography (EEG…
A Random Matrix Perspective of Echo State Networks: From Precise Bias--Variance Characterization to Optimal Regularization
Yessin Moakher, Malik Tiomoko, Cosme Louart +1
We present a rigorous asymptotic analysis of Echo State Networks (ESNs) in a teacher student setting with a linear teacher with oracle weights. Leveraging random matrix theory, we…