NeurIPS 2024poster1 citations
Practical Shuffle Coding
Julius Kunze, Daniel Severo, Jan-Willem van de Meent, James Townsend
Abstract
We present a general method for lossless compression of unordered data structures, including multisets and graphs. It is a variant of shuffle coding that is many orders of magnitude faster than the original and enables 'one-shot' compression of single unordered objects. Our method achieves state-of-the-art compression rates on various large-scale network graphs at speeds of megabytes per second, efficiently handling even a multi-gigabyte plain graph with one billion edges. We release an implementation that can be easily adapted to different data types and statistical models.
graph compressionentropy codingbits-back codinglossless compressiongenerative modelsinformation theoryprobabilistic modelsgraph neural networksmultiset compressionasymmetric numeral systemscompressionentropyshuffle coding
BibTeX
@inproceedings{
kunze2024practical,
title={Practical Shuffle Coding},
author={Julius Kunze and Daniel Severo and Jan-Willem van de Meent and James Townsend},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m2DaXpCoIi}
}