This paper introduces Gumbel-Sinkhorn networks for learning latent matchings.
problem Learning in latent variable models with permutations is difficult due to combinatorial intractability.
method Approximates maximum-weight matching using the Sinkhorn operator, extending Gumbel-Softmax.
result Demonstrates effectiveness on sorting, jigsaw puzzles, and neural signal identification tasks.