Data for free: Fewer-shot algorithm learning with parametricity data augmentation

TitleData for free: Fewer-shot algorithm learning with parametricity data augmentation
Publication TypeConference Paper
Year of Publication2019
AuthorsLewis, O, Hermann, K
Conference NameICLR 2019
Date Published04/2019
Abstract

We address the problem of teaching an RNN to approximate list-processing algorithms given a small number of input-output training examples. Our approach is to generalize the idea of parametricity from programming language theory to formulate a semantic property that distinguishes common algorithms from arbitrary non-algorithmic functions. This characterization leads naturally to a learned data augmentation scheme that encourages RNNs to learn algorithmic behavior and enables small-sample learning in a variety of list-processing tasks.

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