We introduce a simple permutation equivariant layer for deep learning with set structure.This type of layer, obtained by parameter-sharing, has a simple implementation and linear-time complexity in the size of each set. We use deep permutation-invariant networks to perform point-could classification and MNIST-digit summation, where in both cases the output is invariant to permutations of the input. In a semi-supervised setting, where the goal is make predictions for each instance within a set, we demonstrate the usefulness of this type of layer in set-outlier detection as well as semi-supervised learning with clustering side-information.
@article{arxiv.1611.04500,
title = {Deep Learning with Sets and Point Clouds},
author = {Siamak Ravanbakhsh and Jeff Schneider and Barnabas Poczos},
journal= {arXiv preprint arXiv:1611.04500},
year = {2017}
}