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Neural Program Synthesis with Priority Queue Training

We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generate... Full description

Journal Title: arXiv.org Mar 23, 2018
Main Author: Abolafia, Daniel
Other Authors: Norouzi, Mohammad , Shen, Jonathan , Zhao, Rui , Le, Quoc
Format: Electronic Article Electronic Article
Language: English
Subjects:
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recordid: proquest2071884025
title: Neural Program Synthesis with Priority Queue Training
format: Article
creator:
  • Abolafia, Daniel
  • Norouzi, Mohammad
  • Shen, Jonathan
  • Zhao, Rui
  • Le, Quoc
subjects:
  • Rationing
  • Algorithms
  • Social Costs
  • Genetic Algorithms
  • Machine Learning
  • Synthesis
  • Programming Languages
  • Queues
  • Optimization
  • Training
ispartof: arXiv.org, Mar 23, 2018
description: We consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated...
language: eng
source: © ProQuest LLC All rights reserved
identifier:
fulltext: fulltext_linktorsrc
url: Link


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ispartofarXiv.org, Mar 23, 2018
subjectRationing ; Algorithms ; Social Costs ; Genetic Algorithms ; Machine Learning ; Synthesis ; Programming Languages ; Queues ; Optimization ; Training
descriptionWe consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated...
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titleNeural Program Synthesis with Priority Queue Training
descriptionWe consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated...
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titleNeural Program Synthesis with Priority Queue Training
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abstractWe consider the task of program synthesis in the presence of a reward function over the output of programs, where the goal is to find programs with maximal rewards. We employ an iterative optimization scheme, where we train an RNN on a dataset of K best programs from a priority queue of the generated...
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date2018-03-23