  Guided by critical systems found in nature we develop a novel mechanism
consisting of inhomogeneous polynomial regularisation via which we can induce
scale invariance in deep learning systems. Technically, we map our deep
learning (DL) setup to a genuine field theory, on which we act with the
Renormalisation Group (RG) in momentum space and produce the flow equations of
the couplings; those are translated to constraints and consequently interpreted
as "critical regularisation" conditions in the optimiser; the resulting
equations hence prove to be sufficient conditions for - and serve as an elegant
and simple mechanism to induce scale invariance in any deep learning setup.
