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dc.contributor.authorEllis, Kevin
dc.contributor.authorRitchie, Daniel
dc.contributor.authorSolar-Lezama, Armando
dc.contributor.authorTenenbaum, Joshua B.
dc.date.accessioned2021-12-20T20:16:42Z
dc.date.available2021-11-08T21:02:24Z
dc.date.available2021-12-20T20:16:42Z
dc.date.issued2018
dc.identifier.urihttps://hdl.handle.net/1721.1/137831.2
dc.description.abstract© 2018 Curran Associates Inc.All rights reserved. We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of LAT E X. The model combines techniques from deep learning and program synthesis. We learn a convolutional neural network that proposes plausible drawing primitives that explain an image. These drawing primitives are a specification (spec) of what the graphics program needs to draw. We learn a model that uses program synthesis techniques to recover a graphics program from that spec. These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals. With a graphics program in hand, we can correct errors made by the deep network and extrapolate drawings.en_US
dc.description.sponsorshipNSF (Awards GRFP-1753684, CCF-1231216)en_US
dc.description.sponsorshipDARPA (Grant FA8750-14-2-0242)en_US
dc.description.sponsorshipAFOSR (Award FA9550-16-1-0012)en_US
dc.language.isoen
dc.relation.isversionofhttps://papers.nips.cc/paper/7845-learning-to-infer-graphics-programs-from-hand-drawn-imagesen_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.titleLearning to Infer Graphics Programs from Hand-Drawn Imagesen_US
dc.typeArticleen_US
dc.identifier.citationEllis, Kevin, Ritchie, Daniel, Solar-Lezama, Armando and Tenenbaum, Joshua B. 2018. "Learning to Infer Graphics Programs from Hand-Drawn Images."en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_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.updated2019-07-10T13:27:12Z
dspace.date.submission2019-07-10T13:27:13Z
mit.metadata.statusPublication Information Neededen_US


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