  Machine learning on sets towards sequential output is an important and
ubiquitous task, with applications ranging from language modeling 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.
