ML Anthology

Set2Graph: Learning Graphs from Sets

Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, Yaron Lipman
NeurIPS 2020
/neurips/2020/serviansky2020neurips-set2graph/

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.

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Cite

Text

Serviansky et al. "Set2Graph: Learning Graphs from Sets." Neural Information Processing Systems, 2020.

Markdown

[Serviansky et al. "Set2Graph: Learning Graphs from Sets." Neural Information Processing Systems, 2020.](https://mlanthology.org/neurips/2020/serviansky2020neurips-set2graph/)

BibTeX

@inproceedings{serviansky2020neurips-set2graph,
  title     = {{Set2Graph: Learning Graphs from Sets}},
  author    = {Serviansky, Hadar and Segol, Nimrod and Shlomi, Jonathan and Cranmer, Kyle and Gross, Eilam and Maron, Haggai and Lipman, Yaron},
  booktitle = {Neural Information Processing Systems},
  year      = {2020},
  url       = {https://mlanthology.org/neurips/2020/serviansky2020neurips-set2graph/}
}