RegularizingTowardsPermutationInvariancein RecurrentModels

Neural Information Processing Systems 

Such "permutation invariant" functions have been studied extensively recently. Here we argue that temporal architectures such as RNNs are highly relevant for such problems, despite the inherent dependence of RNNs on order.

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