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arXiv · 1611.01988

Differentiable Functional Program Interpreters

John K. Feser·Marc Brockschmidt·Alexander L. Gaunt·Daniel Tarlow
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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.

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John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow. 2016-11-07. Differentiable Functional Program Interpreters. https://arxiv.org/abs/1611.01988

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