Simplicial 2-Complex Convolutional Neural Networks
Abstract
Recently, neural network architectures have been developed to accommodate when the data has the structure of a graph or, more generally, a hypergraph. While useful, graph structures can be potentially limiting. Hypergraph structures in general do not account for higher order relations between their hyperedges. Simplicial complexes offer a middle ground, with a rich theory to draw on. We develop a convolutional neural network layer on simplicial 2-complexes.
Cite
Text
Bunch et al. "Simplicial 2-Complex Convolutional Neural Networks." NeurIPS 2020 Workshops: TDA_and_Beyond, 2020.Markdown
[Bunch et al. "Simplicial 2-Complex Convolutional Neural Networks." NeurIPS 2020 Workshops: TDA_and_Beyond, 2020.](https://mlanthology.org/neuripsw/2020/bunch2020neuripsw-simplicial/)BibTeX
@inproceedings{bunch2020neuripsw-simplicial,
title = {{Simplicial 2-Complex Convolutional Neural Networks}},
author = {Bunch, Eric and You, Qian and Fung, Glenn and Singh, Vikas},
booktitle = {NeurIPS 2020 Workshops: TDA_and_Beyond},
year = {2020},
url = {https://mlanthology.org/neuripsw/2020/bunch2020neuripsw-simplicial/}
}