Inducing Interpretable Representations with Variational Autoencoders
  We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representations through approximate variational inference. This allows us to both perform reasoning (e.g. classification) under the structural constraints of a given graphical model, and use deep generative models to deal with messy, high-dimensional domains where it is often difficult to model all the variation. Learning in this framework is carried out end-to-end with a variational objective, applying to both unsupervised and semi-supervised schemes. 
N. Siddharth and Brooks Paige and Alban Desmaison and Jan-Willem Van   de Meent and Frank Wood and Noah D. Goodman and Pushm...
stat.ML cs.CV cs.LG