|Title||Data for free: Fewer-shot algorithm learning with parametricity data augmentation|
|Publication Type||Conference Paper|
|Year of Publication||2019|
|Authors||Lewis, O, Hermann, K|
|Conference Name||ICLR 2019|
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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