AISTATS 2022poster30 citations
Permutation Equivariant Layers for Higher Order Interactions
Abstract
Recent work on permutation equivariant neural networks has mostly focused on the first order case (sets) and second order case (graphs). We describe the machinery for generalizing permutation equivariance to arbitrary $k$-ary interactions between entities for any value of $k$. We demonstrate the effectiveness of higher order permutation equivariant models on several real world applications and find that our results compare favorably to existing permutation invariant/equivariant baselines.
BibTeX
@InProceedings{pmlr-v151-pan22a,
title = { Permutation Equivariant Layers for Higher Order Interactions },
author = {Pan, Horace and Kondor, Risi},
booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
pages = {5987--6001},
year = {2022},
editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
volume = {151},
series = {Proceedings of Machine Learning Research},
month = {28--30 Mar},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v151/pan22a/pan22a.pdf},
url = {https://proceedings.mlr.press/v151/pan22a.html},
abstract = { Recent work on permutation equivariant neural networks has mostly focused on the first order case (sets) and second order case (graphs). We describe the machinery for generalizing permutation equivariance to arbitrary $k$-ary interactions between entities for any value of $k$. We demonstrate the effectiveness of higher order permutation equivariant models on several real world applications and find that our results compare favorably to existing permutation invariant/equivariant baselines. }
}