  Neural processes (NPs) aim to stochastically complete unseen data points
based on a given context dataset. NPs essentially leverage a given dataset as a
context representation to derive a suitable identifier for a novel task. To
improve the prediction accuracy, many variants of NPs have investigated context
embedding approaches that generally design novel network architectures and
aggregation functions satisfying permutation invariant. In this work, we
propose a stochastic attention mechanism for NPs to capture appropriate context
information. From the perspective of information theory, we demonstrate that
the proposed method encourages context embedding to be differentiated from a
target dataset, allowing NPs to consider features in a target dataset and
context embedding independently. We observe that the proposed method can
appropriately capture context embedding even under noisy data sets and
restricted task distributions, where typical NPs suffer from a lack of context
embeddings. We empirically show that our approach substantially outperforms
conventional NPs in various domains through 1D regression, predator-prey model,
and image completion. Moreover, the proposed method is also validated by
MovieLens-10k dataset, a real-world problem.
