Spatio-temporal forecasting models typically generate predictions only at locations where sensors are physically installed, limiting their applicability in scenarios requiring full spatial coverage. Many real-world applications, […]

In this article, we present a contribution from CEA-List to the TextMine’25 challenge, focusing on two main extensions of the AT-LOP model, one focused on taking […]

This paper addresses the challenge of reducing annotation costs in distributed learning environments, particularly in systems with limited data and computational resources, such as those found […]

This paper proposes a unified framework for analyzing cryptocurrency transaction networks, employing a composite approach that combines key actor identification and change point detection. At first, […]

In the domain of Federated Learning (FL), the issue of managing variability in model architectures surpasses a mere technical barrier, representing a crucial aspect of the […]

The EvalLLM2024 challenge aims to evaluate the results of few-shot approaches to information extraction in French. Our contribution to this challenge tests two approaches: one exploits […]