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Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem.
The role of metalearning in study processes
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Bayesian learning via stochastic gradient langevin dynamics
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Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
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Matching networks for one shot learning
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Discriminative k-shot learning using probabilistic models
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A developmental approach to machine learning?
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Prototypical networks for few-shot learning
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Recasting gradient-based meta-learning as hierarchical bayes
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