  Collisions at high-energy particle colliders are a traditionally fruitful
source of exotic particle discoveries. Finding these rare particles requires
solving difficult signal-versus-background classification problems, hence
machine learning approaches are often used. Standard approaches have relied on
`shallow' machine learning models that have a limited capacity to learn complex
non-linear functions of the inputs, and rely on a pain-staking search through
manually constructed non-linear features. Progress on this problem has slowed,
as a variety of techniques have shown equivalent performance. Recent advances
in the field of deep learning make it possible to learn more complex functions
and better discriminate between signal and background classes. Using benchmark
datasets, we show that deep learning methods need no manually constructed
inputs and yet improve the classification metric by as much as 8\% over the
best current approaches. This demonstrates that deep learning approaches can
improve the power of collider searches for exotic particles.
