  Neural networks (NNs) have been shown to be competitive against
state-of-the-art feature engineering and random forest (RF) classification of
periodic variable stars. Although previous work utilising NNs has made use of
periodicity by period folding multiple-cycle time-series into a single cycle --
from time-space to phase-space -- no approach to date has taken advantage of
the fact that network predictions should be invariant to the initial phase of
the period-folded sequence. Initial phase is exogenous to the physical origin
of the variability and should thus be factored out. Here, we present
cyclic-permutation invariant networks, a novel class of NNs for which
invariance to phase shifts is guaranteed through polar coordinate convolutions,
which we implement by means of "Symmetry Padding." Across three different
datasets of variable star light curves, we show that two implementations of the
cyclic-permutation invariant network: the iTCN and the iResNet, consistently
outperform non-invariant baselines and reduce overall error rates by between 4%
to 22%. Over a 10-class OGLE-III sample, the iTCN/iResNet achieves an average
per-class accuracy of 93.4%/93.3%, compared to RNN/RF accuracies of 70.5%/89.5%
in a recent study using the same data. Finding improvement on a non-astronomy
benchmark, we suggest that the methodology introduced here should also be
applicable to a wide range of science domains where periodic data abounds due
to physical symmetries.
