  Equivariant deep learning architectures exploit symmetries in learning
problems to improve the sample efficiency of neural-network-based models and
their ability to generalise. However, when modelling real-world data, learning
problems are often not exactly equivariant, but only approximately. For
example, when estimating the global temperature field from weather station
observations, local topographical features like mountains break translation
equivariance. In these scenarios, it is desirable to construct architectures
that can flexibly depart from exact equivariance in a data-driven way. In this
paper, we develop a general approach to achieving this using existing
equivariant architectures. Our approach is agnostic to both the choice of
symmetry group and model architecture, making it widely applicable. We consider
the use of approximately equivariant architectures in neural processes (NPs), a
popular family of meta-learning models. We demonstrate the effectiveness of our
approach on a number of synthetic and real-world regression experiments,
demonstrating that approximately equivariant NP models can outperform both
their non-equivariant and strictly equivariant counterparts.
