Neural Information Processing Systems Foundation, Inc.
Abstract
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations.
Our model is provably exchangeable, meaning that the joint distribution over observations is invariant under permutation: this property lies at the heart of Bayesian
inference. The model does not require variational approximations to train, and new
samples can be generated conditional on previous samples, with cost linear in the
size of the conditioning set. The advantages of our architecture are demonstrated
on learning tasks that require generalisation from short observed sequences while
modelling sequence variability, such as conditional image generation, few-shot
learning, and anomaly detectio