  Recent literature on deep neural networks for tagging of highly energetic
jets resulting from top quark decays has focused on image based techniques or
multivariate approaches using high-level jet substructure variables. Here, a
sequential approach to this task is taken by using an ordered sequence of jet
constituents as training inputs. Unlike the majority of previous approaches,
this strategy does not result in a loss of information during pixelisation or
the calculation of high level features. The jet classification method achieves
a background rejection of 45 at a 50% efficiency operating point for
reconstruction level jets with transverse momentum range of 600 to 2500 GeV and
is insensitive to multiple proton-proton interactions at the levels expected
throughout Run 2 of the LHC.
