1 citations · 1 across the 3 of their papers we have counts for
4 papers
Mantis: Lightweight Foundation Model for Time Series Classification
Vasilii Feofanov, Songkang Wen, Shifeng Xie +10
While foundation models have revolutionized various domains, their application to time series classification remains rather under-explored, with existing literature predominantly f…
User-friendly Foundation Model Adapters for Multivariate Time Series Classification
Vasilii Feofanov, Romain Ilbert, Malik Tiomoko +2
Foundation models, while highly effective, are often resource-intensive, requiring substantial inference time and memory. This paper addresses the challenge of making these models…
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
Romain Ilbert, Malik Tiomoko, Cosme Louart +4
In this paper, we introduce a novel theoretical framework for multi-task regression, applying random matrix theory to provide precise performance estimations, under high-dimensiona…
Data Augmentation for Multivariate Time Series Classification: An Experimental Study
Romain Ilbert, Thai V. Hoang, Zonghua Zhang
Our study investigates the impact of data augmentation on the performance of multivariate time series models, focusing on datasets from the UCR archive. Despite the limited size of…