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Comment by timtadh | Hacker News Reader

@suryabhupa How similar is this work to the Grammatical Inference field? There has been a lot of work over the years in specification inference which feels similar. Many of the studies in specification inference learn automata representations of object interactions. I know there have been other application grammatical inference in Software Engineering as well.

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Program synthesis is grammatical inference grown up, and statistical approaches are being experimented w/ for modern synthesis just as they were for the genetic programming & grammatical inference era. (I believe even now at the SAT solver level today.)

At a quick skim, this seems fun more as (1) an experience report of jumping on the DNN train instead of other ML algs and (2) more intriguing to me, the training formulation (irrespective of neural nets). Dawn Song's recent explorations here also sounded pretty interesting in terms of bridging logical synthesis of general programs with statistical..

Grammatical inference learns a grammar from a set of examples, where here it seems the paper is learning a program (a derivation in the grammar) from examples.

Which Dawn Song paper are you talking about here? I think among all the recent approaches proposed recently on neural program induction, this is the first one that is end-to-end trained and learns only from input-output examples without any hacks!

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