2 papers
cs.LG2026
CAARL: In-Context Learning for Interpretable Co-Evolving Time Series Forecasting
Etienne Tajeuna, Patrick Asante Owusu, Armelle Brun +1
In this paper we investigate forecasting coevolving time series that feature intricate dependencies and nonstationary dynamics by using an LLM Large Language Models approach We pro…
cs.LG2025
GRAVITY: A Controversial Graph Representation Learning for Vertex Classification
Etienne Gael Tajeuna, Jean Marie Tshimula
In the quest of accurate vertex classification, we introduce GRAVITY (Graph-based Representation leArning via Vertices Interaction TopologY), a framework inspired by physical syste…