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Forward-Backward Splitting for Optimal Transport based Problems
Experiments show that the proposed method leads to a significant improvement in terms of speed and performance with respect to the state of the art for domain adaptation on a continually rotating distribution coming from the standard two moon dataset.
node2coords: Graph Representation Learning with Wasserstein Barycenters
In the decoder, the node connectivity descriptors are reconstructed as Wasserstein barycenters of the graph structural patterns.
FlowPool: Pooling Graph Representations with Wasserstein Gradient Flows
This is achieved by performing a Wasserstein gradient flow with respect to the pooled graph representation.
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