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[D] Does Neural Program Synthesis be improved with x100 scaling of samples/compute/labels/curriculum ?!

Looking at some recent papers on program synthesis

Neural (Meta) Program Synthesis, Singh {GB}

AlphaNPI twitted that acceptance to NIPS2019 with spotlight

I am wondering if field is still working out good architectures, representations, etc

OR

existing SOTA techniques can be applied if we have x100 more compute, or a massive dataset of input-output pairs, or maybe a long detailed curated curriculum of specs and solutions, etc

submitted by /u/so_tiredso_tired
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.