Related papers: Neural Program Synthesis with a Differentiable Fixer

Neural Program Synthesis with a Differentiable Fixer

URL: http://arxiv.org/abs/2006.10924v1
Date: Fri, 19 Jun 2020 01:49:46 GMT
Title: Neural Program Synthesis with a Differentiable Fixer
Authors: Matej Balog, Rishabh Singh, Petros Maniatis, Charles Sutton
Abstract summary: We present a new program synthesis approach that combines an encoder-decoder based synthesis architecture with a differentiable program fixer. We train our architecture end-to-end on the RobustFill domain, and show that the addition of the fixer module leads to a significant improvement on synthesis accuracy.
Score: 44.48509453344902
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We present a new program synthesis approach that combines an encoder-decoder based synthesis architecture with a differentiable program fixer. Our approach is inspired from the fact that human developers seldom get their program correct on the first attempt, and perform iterative testing-based program fixing to get to the desired program functionality. Similarly, our approach first learns a distribution over programs conditioned on an encoding of a set of input-output examples, and then iteratively performs fix operations using the differentiable fixer. The fixer takes as input the original examples and the current program's outputs on example inputs, and generates a new distribution over the programs with the goal of reducing the discrepancies between the current program outputs and the desired example outputs. We train our architecture end-to-end on the RobustFill domain, and show that the addition of the fixer module leads to a significant improvement on synthesis accuracy compared to using beam search.

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