  Deep learning algorithms will play a key role in the upcoming runs of the
Large Hadron Collider (LHC), helping bolster various fronts ranging from fast
and accurate detector simulations to physics analysis probing possible
deviations from the Standard Model. The game-changing feature of these new
algorithms is the ability to extract relevant information from high-dimensional
input spaces, often regarded as "replacing the expert" in designing
physics-intuitive variables. While this may seem true at first glance, it is
far from reality. Existing research shows that physics-inspired feature
extractors have many advantages beyond improving the qualitative understanding
of the extracted features. In this review, we systematically explore automatic
feature extraction from a phenomenological viewpoint and the motivation for
physics-inspired architectures. We also discuss how prior knowledge from
physics results in the naturalness of the point cloud representation and
discuss graph-based applications to LHC phenomenology.
