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Found 4 papers, 1 papers with code
A Variational Autoencoder for Heterogeneous Temporal and Longitudinal Data
In this work, we propose the heterogeneous longitudinal VAE (HL-VAE) that extends the existing temporal and longitudinal VAEs to heterogeneous data.
Learning Conditional Variational Autoencoders with Missing Covariates
Conditional variational autoencoders (CVAEs) are versatile deep generative models that extend the standard VAE framework by conditioning the generative model with auxiliary covariates.
Longitudinal Variational Autoencoder
Longitudinal datasets measured repeatedly over time from individual subjects, arise in many biomedical, psychological, social, and other studies.
Latent Gaussian process with composite likelihoods and numerical quadrature
Clinical patient records are an example of high-dimensional data that is typically collected from disparate sources and comprises of multiple likelihoods with noisy as well as missing values.
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