A crossover code for high-dimensional composition
Neurons and Cognition
2019-11-18 v1
Abstract
We present a novel way to encode compositional information in high-dimensional (HD) vectors. Inspired by chromosomal crossover, random HD vectors are recursively interwoven, with a fraction of one vector's components masked out and replaced by those from another using a context-dependent mask. Unlike many HD computing schemes, "crossover" codes highly overlap with their base elements' and sub-structures' codes without sacrificing relational information, allowing fast element readout and decoding by greedy reconstruction. Crossover is mathematically tractable and has several properties desirable for robust, flexible representation.
Keywords
Cite
@article{arxiv.1911.06775,
title = {A crossover code for high-dimensional composition},
author = {Rich Pang},
journal= {arXiv preprint arXiv:1911.06775},
year = {2019}
}
Comments
Presented at NeurIPS 2019 Workshop on Context and Compositionality in Biological and Artificial Neural Systems