# Adaptive Neural Compilation

arXiv: https://arxiv.org/abs/1605.07969 · HTML: https://mlsanity.com/paper/1605.07969/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2016.

## Built on by (most cited first, 3 of 3)

- [Unsupervised Learning of Neurosymbolic Encoders](https://mlsanity.com/paper/2107.13132/) (2021)
- [Algorithmic Language Models with Neurally Compiled Libraries](https://mlsanity.com/paper/2407.04899/) (2024)
- [Geometry of Program Synthesis](https://mlsanity.com/paper/2103.16080/) (2021)

## Abstract (arXiv)

This paper proposes an adaptive neural-compilation framework to address the problem of efficient program learning. Traditional code optimisation strategies used in compilers are based on applying pre-specified set of transformations that make the code faster to execute without changing its semantics. In contrast, our work involves adapting programs to make them more efficient while considering correctness only on a target input distribution. Our approach is inspired by the recent works on differentiable representations of programs. We show that it is possible to compile programs written in a low-level language to a differentiable representation. We also show how programs in this representation can be optimised to make them efficient on a target distribution of inputs. Experimental results demonstrate that our approach enables learning specifically-tuned algorithms for given data distributions with a high success rate.