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BayesianOptimizationofFunctionNetworks: SupplementaryMaterial
Neural Information Processing Systems
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WassersteinIterativeNetworks forBarycenterEstimation
Neural Information Processing Systems
Wasserstein barycenters have become popular due to their ability to represent the average of probability measures in a geometrically meaningful way. In this paper, we present an algorithm to approximate the Wasserstein-2 barycenters of continuous measures via a generative model.
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ObservationPrediction Matching Area
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Quo Vadis: Is Trajectory Forecastingthe Key Towards Long-Term Multi-Object Tracking?
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SE(3)-equivariantpredictionofmolecular wavefunctionsandelectronicdensities
Neural Information Processing Systems
B: Overview of the proposed PhiSNet architecture.
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