@inproceedings{feser2017differentiable,
author = {Feser, John K. and Brockschmidt, Marc and Gaunt, Alexander and Tarlow, Daniel},
title = {Differentiable Functional Program Interpreters},
year = {2017},
month = {March},
abstract = {Programming by Example (PBE) is the task of inducing computer programs from input-output examples. It can be seen as a type of machine learning where the hypothesis space is the set of legal programs in some programming language. Recent work on differentiable interpreters relaxes the discrete space of programs into a continuous space so that search over programs can be performed using gradient-based optimization. While conceptually powerful, so far differentiable interpreter-based program synthesis has only been capable of solving very simple problems. In this work, we study modeling choices that arise when constructing a differentiable programming language and their impact on the success of synthesis. The main motivation for the modeling choices comes from functional programming: we study the effect of memory allocation schemes, immutable data, type systems, and built-in control-flow structures. Empirically we show that incorporating functional programming ideas into differentiable programming languages allows us to learn much more complex programs than is possible with existing differentiable languages.},
url = {http://approjects.co.za/?big=en-us/research/publication/differentiable-functional-program-interpreters/},
}