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Neural Processes (NPs) are a family of conditional generative models that are able to model a distribution over functions, in a way that allows them to perform predictions at test time conditioned on a number of context points.
Ecological role of Volterra’s equations
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Orthogonally decoupled variational gaussian processes
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Generalizing to unseen domains via adversarial data augmentation
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Attentive neural processes
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The functional neural process
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Sequential neural processes
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Exact gaussian processes on a million data points
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Convolutional conditional neural processes
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