Deep Learning vs Structured Learning

I am interested in the differences between using large, deep learning networks vs Probabilistic graphical models (PGMs), like Random Field models, for structured learning (e.g. on images, or labels of arbitrary graphs on surfaces, etc...). For instance, what areas/fields/problems would one or the other be preferred? Are there theoretical guarantees for one vs another? How can they be used together (e.g. in papers like this)? I have heard lately that deep learning (i.e.