Dynamical systems that evolve continuously over time are ubiquitous throughout science
and engineering. Machine learning (ML) provides data-driven approaches to model and …

The neural Ordinary Differential Equation (ODE) model has shown success in learning
complex continuous-time processes from observations on discrete time stamps. In this work …

In railway operations, several factors must be analyzed, such as operation cost,
maintenance stops, failures, and others. One of these important topics is the analysis of the …

Learning dynamics governed by differential equations is crucial for predicting and
controlling the systems in science and engineering. Neural Ordinary Differential Equation …

The recent rise of deep learning has been motivated by numerous scientific breakthroughs,
particularly regarding representation learning and generative modeling. However, most of …

Résumé L'essor de l'apprentissage profond trouve notamment sa source dans les avancées
scientifiques qu'il a permises en termes d'apprentissage de représentations et de modèles …