  Structured variational autoencoders (SVAEs) combine probabilistic graphical
model priors on latent variables, deep neural networks to link latent variables
to observed data, and structure-exploiting algorithms for approximate posterior
inference. These models are particularly appealing for sequential data, where
the prior can capture temporal dependencies. However, despite their conceptual
elegance, SVAEs have proven difficult to implement, and more general approaches
have been favored in practice. Here, we revisit SVAEs using modern machine
learning tools and demonstrate their advantages over more general alternatives
in terms of both accuracy and efficiency. First, we develop a modern
implementation for hardware acceleration, parallelization, and automatic
differentiation of the message passing algorithms at the core of the SVAE.
Second, we show that by exploiting structure in the prior, the SVAE learns more
accurate models and posterior distributions, which translate into improved
performance on prediction tasks. Third, we show how the SVAE can naturally
handle missing data, and we leverage this ability to develop a novel,
self-supervised training approach. Altogether, these results show that the time
is ripe to revisit structured variational autoencoders.
