  Conditional Neural Processes (CNPs) are a class of metalearning models
popular for combining the runtime efficiency of amortized inference with
reliable uncertainty quantification. Many relevant machine learning tasks, such
as in spatio-temporal modeling, Bayesian Optimization and continuous control,
inherently contain equivariances -- for example to translation -- which the
model can exploit for maximal performance. However, prior attempts to include
equivariances in CNPs do not scale effectively beyond two input dimensions. In
this work, we propose Relational Conditional Neural Processes (RCNPs), an
effective approach to incorporate equivariances into any neural process model.
Our proposed method extends the applicability and impact of equivariant neural
processes to higher dimensions. We empirically demonstrate the competitive
performance of RCNPs on a large array of tasks naturally containing
equivariances.
