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We have been working on this recently on the Google Brain team. We are working both on synthesizing programs from scratch (see https://arxiv.org/abs/2002.09030
by tasdfqwer0897 6y ago
We have been working on this recently on the Google Brain team.
We are working both on synthesizing programs from scratch
(see https://arxiv.org/abs/2002.09030 https://arxiv.org/abs/2002.09030 for example) and
on understanding computer programs using machine learning
(see e.g. https://arxiv.org/abs/1911.01205 https://arxiv.org/abs/1911.01205).
I'm always happy to correspond with people about these topics.
- yazr 6y agoLove to chat. Sent u an email (former HFT and kernel guy). One topic which is feel is neglected is a good GCN (or any GNN) to operate on existing code trees. Most approaches seem to prefer seq or at most tree inputs. Is this simply not finding yet a good network architecture, or is it a performance issue ?
- siv_ran7611 6y agoI dont see any email listed on your profile. Would like to have a chat to work/learn on this stuff
- logicchains 6y agoHave you considered attempting to develop a "differentiable programming language" so that it's possible to do gradient descent directly on the space of programs? There was some work on building a "differentiable lambda calculus", https://core.ac.uk/download/pdf/82396223.pdf https://core.ac.uk/download/pdf/82396223.pdf, but I haven't heard of anyone trying to use something like that in modern program synthesis.
- m3at 6y agoLate on the thread but you might be interested in this: https://www.tensorflow.org/swift https://www.tensorflow.org/swift
- logicchains 6y agoThat's something different: it allows doing gradient descent on the space of paramaters for programs, not on the space of programs.