  We review a novel neural network architecture called lattice gauge
equivariant convolutional neural networks (L-CNNs), which can be applied to
generic machine learning problems in lattice gauge theory while exactly
preserving gauge symmetry. We discuss the concept of gauge equivariance which
we use to explicitly construct a gauge equivariant convolutional layer and a
bilinear layer. The performance of L-CNNs and non-equivariant CNNs is compared
using seemingly simple non-linear regression tasks, where L-CNNs demonstrate
generalizability and achieve a high degree of accuracy in their predictions
compared to their non-equivariant counterparts.
