Set-to-Sequence Methods in Machine Learning: A Review
Abstract
Machine learning on sets towards sequential output is an important and ubiquitous task, with applications ranging from language modelling and meta-learning to multi-agent strategy games and power grid optimization. Combining elements of representation learning and structured prediction, its two primary challenges include obtaining a meaningful, permutation invariant set representation and subsequently utilizing this representation to output a complex target permutation. This paper provides a comprehensive introduction to the _eld as well as an overview of important machine learning methods tackling both of these key challenges, with a detailed qualitative comparison of selected model architectures.
Cite
Text
Jurewicz and Derczynski. "Set-to-Sequence Methods in Machine Learning: A Review." Journal of Artificial Intelligence Research, 2021. doi:10.1613/JAIR.1.12839Markdown
[Jurewicz and Derczynski. "Set-to-Sequence Methods in Machine Learning: A Review." Journal of Artificial Intelligence Research, 2021.](https://mlanthology.org/jair/2021/jurewicz2021jair-settosequence/) doi:10.1613/JAIR.1.12839BibTeX
@article{jurewicz2021jair-settosequence,
title = {{Set-to-Sequence Methods in Machine Learning: A Review}},
author = {Jurewicz, Mateusz and Derczynski, Leon},
journal = {Journal of Artificial Intelligence Research},
year = {2021},
pages = {885-924},
doi = {10.1613/JAIR.1.12839},
volume = {71},
url = {https://mlanthology.org/jair/2021/jurewicz2021jair-settosequence/}
}