资源分类
》 资源 》论文》Library Learning for Neurally-Guided Bayesian Program Induction

Library Learning for Neurally-Guided Bayesian Program Induction

2020-02-13 | |  74 |   50 |   0

Abstract

 Successful approaches to program induction require a hand-engineered domainspecific language (DSL), constraining the space of allowed programs and imparting prior knowledge of the domain. We contribute a program induction algorithm called EC2 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.

上一篇：Multi-objective Maximization of Monotone Submodular Functions with Cardinality Constraint

下一篇：Middle-Out Decoding

用户评价
全部评价

热门资源

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...