@conference {4188,
	title = {Data for free: Fewer-shot algorithm learning with parametricity data augmentation},
	booktitle = {ICLR 2019},
	year = {2019},
	month = {04/2019},
	abstract = {<p>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.</p>
},
	author = {Owen Lewis and Katherine Hermann}
}
