ICLR 2017workshop13 citations
Neural Functional Programming
John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow
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.
Supervised Learning
BibTeX
@misc{
lee2017making,
title={Making Stochastic Neural Networks from Deterministic Ones},
author={Kimin Lee and Jaehyung Kim and Song Chong and Jinwoo Shin},
year={2017},
url={https://openreview.net/forum?id=B1akgy9xx}
}