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Wasserstein Neural Processes
Neural Processes (NPs) are a class of models that learn a mapping from a context set of input-output pairs to a distribution over functions.
Graph Neural Processes: Towards Bayesian Graph Neural Networks
We introduce Graph Neural Processes (GNP), inspired by the recent work in conditional and latent neural processes.
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