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Hierarchical Bayesian methods can unify many related tasks (e.g.
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Model-agnostic meta-learning for fast adaptation of deep networks
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Learning to remember rare events
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Prototypical networks for few-shot learning
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Few-shot learning through an information retrieval lens
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