NeurIPS 2020poster44 citations
Set2Graph: Learning Graphs From Sets
Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman
Abstract
Many problems in machine learning (ML) can be cast as learning functions from sets to graphs, or more generally to hypergraphs; in short, Set2Graph functions. Examples include clustering, learning vertex and edge features on graphs, and learning features on triplets in a collection.
BibTeX
@inproceedings{NEURIPS2020_fb4ab556,
author = {Serviansky, Hadar and Segol, Nimrod and Shlomi, Jonathan and Cranmer, Kyle and Gross, Eilam and Maron, Haggai and Lipman, Yaron},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
pages = {22080--22091},
publisher = {Curran Associates, Inc.},
title = {Set2Graph: Learning Graphs From Sets},
url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/fb4ab556bc42d6f0ee0f9e24ec4d1af0-Paper.pdf},
volume = {33},
year = {2020}
}