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 detection.
The BRUNO model REF performs exact Bayesian inference on sets of data such that the joint distribution over observations is permutation invariant in an autoregressive fashion, in that new samples can be generated conditional on previous ones and a stream of new data points can be easily incorporated at test time.
BRUNO: A Deep Recurrent Model for Exchangeable Data
NeurIPS
Mathematics Computer Science