Open Peer Review. Open Publishing. Open Access. Open Discussion. Open Directory. Open Recommendations. Open API. Open Source.
Neural Functional Programming
John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow
Feb 17, 2017 (modified: Feb 17, 2017)ICLR 2017 workshop submissionreaders: everyone
Abstract:We discuss a range of modeling choices that arise when constructing an end-to-end differentiable programming language suitable for learning programs from input-output examples. Taking cues from programming languages research, we study the effect of memory allocation schemes, immutable data, type systems, and built-in control-flow structures on the success rate of learning algorithms. We build a range of models leading up to a simple differentiable functional programming language. Our empirical evaluation shows that this language allows to learn far more programs than existing baselines.
TL;DR:A differentiable functional programming language for learning programs from input-output examples.
Enter your feedback below and we'll get back to you as soon as possible.