Neural network approaches for meta-learning distributions over functions have desirable properties such as increased flexibility and reduced inference computational complexity. Building on the successes of denoising diffusion models for generative modelling, we propose Neural Diffusion Processes (NDPs), a novel approach that learns to sample from a rich distribution over functions. By introducing a novel attention block we are able to incorporate properties of stochastic processes, such as excha…