  Sequence-based modeling broadly refers to algorithms that act on data that is
represented as an ordered set of input elements. In particular, Machine
Learning algorithms with sequences as inputs have seen successfull applications
to important problems, such as Natural Language Processing (NLP) and speech
signal modeling. The usage this class of models in collider physics leverages
their ability to act on data with variable sequence lengths, such as
constituents inside a jet. In this document, we explore the application of
Recurrent Neural Networks (RNNs) and other sequence-based neural network
architectures to classify jets, regress jet-related quantities and to build a
physics-inspired jet representation, in connection to jet clustering
algorithms. In addition, alternatives to sequential data representations are
briefly discussed.
