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dc.contributor.authorEllis, Kevin M.
dc.contributor.authorMorales, Lucas E.
dc.contributor.authorSable-Meyer, Mathias
dc.contributor.authorSolar Lezama, Armando
dc.contributor.authorTenenbaum, Joshua B
dc.date.accessioned2019-07-11T20:40:16Z
dc.date.available2019-07-11T20:40:16Z
dc.date.issued2018
dc.identifier.urihttps://hdl.handle.net/1721.1/121592
dc.description.abstractSuccessful approaches to program induction require a hand-engineered domain-specific language (DSL), constraining the space of allowed programs and imparting prior knowledge of the domain. We contribute a program induction algorithm called EC 2 that learns a DSL while jointly training a neural network to efficiently search for programs in the learned DSL. We use our model to synthesize functions on lists, edit text, and solve symbolic regression problems, showing how the model learns a domain-specific library of program components for expressing solutions to problems in the domain.en_US
dc.description.sponsorshipAir Force Office of Scientific Research (Grant FA9550-16-1-0012)en_US
dc.description.sponsorshipUnited States. Defense Advanced Research Projects Agency (Grant FA8750-14-2-0242)en_US
dc.language.isoen
dc.publisherCurran Associatesen_US
dc.relation.isversionofhttps://papers.nips.cc/paper/8006-learning-libraries-of-subroutines-for-neurallyguided-bayesian-program-inductionen_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceNeural Information Processing Systems (NIPS)en_US
dc.titleLibrary learning for neurally-guided Bayesian program inductionen_US
dc.typeArticleen_US
dc.identifier.citationEllis, Kevin et al. "Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Induction." Advances in Neural Information Processing Systems 31 (NIPS 2018) © 2018 Curran Associates Incen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.relation.journalAdvances in Neural Information Processing Systems 31 (NIPS 2018)en_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-07-10T13:35:15Z
dspace.date.submission2019-07-10T13:35:16Z


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