  Over the last few years, Neural Processes have become a useful modelling tool
in many application areas, such as healthcare and climate sciences, in which
data are scarce and prediction uncertainty estimates are indispensable.
However, the current state of the art in the field (AR CNPs; Bruinsma et al.,
2023) presents a few issues that prevent its widespread deployment. This work
proposes an alternative, diffusion-based approach to NPs which, through
conditioning on noised datasets, addresses many of these limitations, whilst
also exceeding SOTA performance.
