Set-to-Sequence Methods in Machine Learning: a Review

• URL: http://arxiv.org/abs/2103.09656v1
• Date: Wed, 17 Mar 2021 13:52:33 GMT
• Title: Set-to-Sequence Methods in Machine Learning: a Review
• Authors: Mateusz Jurewicz, Leon Str{\o}mberg-Derczynski
• Abstract summary: 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. This paper provides a comprehensive introduction to the field as well as an overview of important machine learning methods tackling both of these key challenges.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 field 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.

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