Neural Permutation Processes

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Ari Pakman, Yueqi Wang, Liam Paninski ;
Proceedings of The 2nd Symposium on Advances in Approximate Bayesian Inference, PMLR 118:1-7, 2020.

Abstract

We introduce a neural architecture to perform amortized approximate Bayesian inference over latent random permutations of two sets of objects. The method involves approximating permanents of matrices of pairwise probabilities using recent ideas on functions dened over sets. Each sampled permutation comes with a probability estimate, a quantity unavailable in MCMC approaches. We illustrate the method in sets of 2D points and MNIST images.

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