Attentive Sequential Neural Processes
Sequential Neural Processes (SNP) is a new class of models that temporally extends Neural Processes (NP) and can meta learn a sequence of stochastic processes. This learned function however under fits the provided contexts as is also the case in NP. Applying attention to the contexts resolves this in NP but simply extending this to SNP and applying attention on a buffer of context history is sub optimal as our findings show. In this paper, we propose Attentive Sequential Neural Processes (ASNP) which resolves the under fitting in SNP by introducing a novel imaginary context, modeled as a latent variable, over which the attention can then be applied. We evaluate our model on 1D Gaussian Processes regression and 2D moving MNIST/CelebA regression. We apply ASNP to implement Attentive Temporal GQN and further evaluate on the moving CelebA task.