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Type-driven Neural Programming by Example
[article]
2020
arXiv
pre-print
In this thesis we look into programming by example (PBE), which is about finding a program mapping given inputs to given outputs. PBE has traditionally seen a split between formal versus neural approaches, where formal approaches typically involve deductive techniques such as SAT solvers and types, while the neural approaches involve training on sample input-outputs with their corresponding program, typically using sequence-based machine learning techniques such as LSTMs [41]. As a result of
arXiv:2008.12613v5
fatcat:ejaoo63f7rfzbetmlxg4w4q63a