Neural Processes (NPs) are appealing due to their ability to perform fast
adaptation based on a context set. This set is encoded by a latent variable,
which is often assumed to follow a simple distribution. However, in real-word
settings, the context set may be drawn from richer distributions having
multiple modes, heavy tails, etc. In this work, we provide a framework that
allows NPs' latent variable to be given a rich prior defined by a graphical
model. These distributional assumptions directly translate into an appropriate
aggregation strategy for the context set. Moreover, we describe a
message-passing procedure that still allows for end-to-end optimization with
stochastic gradients. We demonstrate the generality of our framework by using
mixture and Student-t assumptions that yield improvements in function modelling
and test-time robustness.

本研究提供一个框架，允许神经过程（NPs）的潜变量被赋予由图形模型定义的丰富先验。将分布假设直接转化为上下文集合的适当聚合策略，并通过消息传递过程进行端到端优化。使用混合和学生 - t 假设证明了该框架的普适性，从而提高了函数建模和测试时的鲁棒性。