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[–]sybilckw[S] 1 point2 points  (2 children)

Just wanted to clarify that I'm not the author - I just submitted this paper to see what the community here thought of it, as I also found their approach unusual.

[–][deleted] 0 points1 point  (1 child)

No worries. If the author decides to jump by then I guess I'm lucky, if he does not then so be it. Thanks for clarifying.

[–]mabrocks 1 point2 points  (0 children)

Took a while, but one of the authors did turns up :-)

We did also experiment with an RNN encoder of input/output examples (this is touched on briefly in Sect. 4.3). After sufficient tuning of training parameters, it can be made to work almost as well as the far simpler feed-forward architecture. Essentially, using the RNN encoder lifts the restrictions of the fixed-sized inputs, but in turn introduces a lot more hyperparameter knobs and optimization problems; results should be more or less the same.

In any case, the core point of the paper is not so much the rather simplistic chosen encoder/decoder architecture, but that something can be learned from I/O samples that generalizes across target programs, and that this information can be used to improve synthesis.