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The linked article is about a novel deep learning approach called Conditional Neural Process (CNP) that can be used for few-shot learning tasks. The CNP model is capable of learning a conditional distribution over functions from a small number of input-output pairs, and can then generate new function values at unseen input locations. The authors demonstrate the effectiveness of the CNP model on various few-shot regression and classification tasks, and show that it outperforms other state-of-the-art few-shot learning methods.