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Library learning for neurally-guided Bayesian program induction

Author(s)
Ellis, Kevin M.; Morales, Lucas E.; Sable-Meyer, Mathias; Solar Lezama, Armando; Tenenbaum, Joshua B
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Article 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.
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Abstract
Successful 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.
Date issued
2018
URI
https://hdl.handle.net/1721.1/121592
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Journal
Advances in Neural Information Processing Systems 31 (NIPS 2018)
Publisher
Curran Associates
Citation
Ellis, 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 Inc
Version: Final published version

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