Unsupervised Learning of 3-Colorings Using Simplicial Higher-Order Neural Networks
Abstract
We propose Higher-Order Networks (HONs) for historically challenging problems for Graph Neural Networks (GNNs), such as Constraint Satisfaction Problems (CSPs). We apply a simple extension of GNNs to HONs and show its advantages for solving 3-coloring.
Cite
Text
Laird et al. "Unsupervised Learning of 3-Colorings Using Simplicial Higher-Order Neural Networks." ICML 2023 Workshops: TAGML, 2023.Markdown
[Laird et al. "Unsupervised Learning of 3-Colorings Using Simplicial Higher-Order Neural Networks." ICML 2023 Workshops: TAGML, 2023.](https://mlanthology.org/icmlw/2023/laird2023icmlw-unsupervised/)BibTeX
@inproceedings{laird2023icmlw-unsupervised,
title = {{Unsupervised Learning of 3-Colorings Using Simplicial Higher-Order Neural Networks}},
author = {Laird, Lucas and Walters, Robin and Gatterbauer, Wolfgang},
booktitle = {ICML 2023 Workshops: TAGML},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/laird2023icmlw-unsupervised/}
}