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Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function.
Gradient-based learning applied to document recognition
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Stochastic backpropagation and approximate inference in deep generative models
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Weight uncertainty in neural networks
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Siamese neural networks for one-shot image recognition
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Human-level concept learning through probabilistic program induction
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Fast adaptation in generative models with generative matching networks
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Building machines that learn and think like people
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Multiplicative normalizing flows for variational bayesian neural networks
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Bayesian compression for deep learning
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Prototypical networks for few-shot learning
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Neural scene representation and rendering
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