Matches in SemOpenAlex for { <https://semopenalex.org/work/W3136268264> ?p ?o ?g. }
- W3136268264 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." @default.
- W3136268264 created "2021-03-29" @default.
- W3136268264 creator A5017448760 @default.
- W3136268264 creator A5034455963 @default.
- W3136268264 date "2021-03-17" @default.
- W3136268264 modified "2023-09-27" @default.
- W3136268264 title "Set-to-Sequence Methods in Machine Learning: a Review" @default.
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