Learning to Represent Programs with Property SignaturesDownload PDF

Sep 25, 2019 (edited Mar 11, 2020)ICLR 2020 Conference Blind SubmissionReaders: Everyone
  • Original Pdf: pdf
  • Keywords: Program Synthesis
  • TL;DR: We represent a computer program using a set of simpler programs and use this representation to improve program synthesis techniques.
  • Abstract: We introduce the notion of property signatures, a representation for programs and program specifications meant for consumption by machine learning algorithms. Given a function with input type τ_in and output type τ_out, a property is a function of type: (τ_in, τ_out) → Bool that (informally) describes some simple property of the function under consideration. For instance, if τ_in and τ_out are both lists of the same type, one property might ask ‘is the input list the same length as the output list?’. If we have a list of such properties, we can evaluate them all for our function to get a list of outputs that we will call the property signature. Crucially, we can ‘guess’ the property signature for a function given only a set of input/output pairs meant to specify that function. We discuss several potential applications of property signatures and show experimentally that they can be used to improve over a baseline synthesizer so that it emits twice as many programs in less than one-tenth of the time.
  • Code: https://github.com/brain-research/searcho
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