Exploring Neural ODEs in Irregular Time Series

This chapter investigates the application of latent ordinary differential equations (ODEs) in managing irregularly sampled medical time series data, particularly in cancer gene assays. It discusses the advantages of continuous time models and the need for improved efficiency in neural ODEs for broader adoption in predictive analytics. The conversation also addresses challenges in effectively utilizing sequential data, the importance of preserving information, and recent advancements in graph neural networks and attention mechanisms.