I will agree with alevskaya that the compilation times were an issue in my particular research ten years ago. I was trying to build neural-networks for parsing that were created at run-time. Since each parse tree had a different computation graph, I was not able to use Theano since it required compiling every single type of parse tree computation graph it encountered during training.
[edit if you want more details: There is really interesting old-school work called "Recursive distributed representations" and later "Labelling recursive auto-associative memory" that used auto-encoders to consume a variable length sequence, e.g. text string, in a sequential fashion. My work with Yoshua Bengio---incomplete---was based upon the idea of doing unsupervised binary parsing of sentences using a hierarchical RAAM-style approach: At any given point in time, greedily find the two adjacent tokens that could be most easily compressed into one token with low reconstruction error. However, once you apply this recursively and end up with auto-encoding binary parse trees, you end up with a variety of different computation graphs, each of which required separate compilation.]
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