Neural Processes (NPs) (Garnelo et al., 2018a;b) approach regression by learning
to map a context set of observed input-output pairs to a distribution over regression
functions. Each function models the distribution of the output given an input, conditioned on the context. NPs have the benefit of fitting observed data efficiently
with linear complexity in the number of context input-output pairs, and can learn
a wide family of conditional distributions; they learn predictive distributions conditioned on context sets of arbitrary size. Nonetheless, we show that NPs suffer a
fundamental drawback of underfitting, giving inaccurate predictions at the inputs
of the observed data they condition on. We address this issue by incorporating
attention into NPs, allowing each input location to attend to the relevant context
points for the prediction. We show that this greatly improves the accuracy of predictions, results in noticeably faster training, and expands the range of functions
that can be modelled.