Variational Deep Alliance: A Generative Auto-Encoding Approach to Longitudinal Data Analysis.

ABSTRACT: Rapid advancements in the field of deep learning have had a profound impact on a wide range of scientific studies. This paper incorporates the power of deep neural networks to learn complex relationships in longitudinal data. The novel generative approach, Variational Deep Alliance (VaDA), is established, where an "alliance" is formed across repeated measurements via the strength of Variational Auto-Encoder. VaDA models the generating process of longitudinal data with a unified and well-structured latent space, allowing outcomes prediction, subjects clustering and representation learning simultaneously. The integrated model can be inferred efficiently within a stochastic Auto-Encoding Variational Bayes framework, which is scalable to large datasets and can accommodate variables of mixed ty

SUBMITTER: Feng S 

PROVIDER: S-EPMC12840063 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature