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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.
Multinomial Variational Autoencoders can recover Principal Components
Covariance estimation on high dimensional data is a central challenge across multiple scientific disciplines.
Longitudinal Variational Autoencoder
Longitudinal datasets measured repeatedly over time from individual subjects, arise in many biomedical, psychological, social, and other studies.
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