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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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Last-IterateConvergenceofOptimisticGradient MethodforMonotoneVariationalInequalities
Neural Information Processing Systems
In 1weintroduce ourmotivations, ourmain results and discuss the related work.
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