The combination of probalistic programming and deep learning is pretty interesting to me because that's what I have going on in two of my work projects.

What we do is have features built using deep learning models, then use that extract simple linear or categorical features which we condition our probalistic model on.

We've found it quite hard to use very high numbers of variables in the probalistic model.

Has anyone found a better way of doing this?