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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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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Advances in Neural Information Processing Systems 31 (NIPS 2018)
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
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