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This paper proves an abstract theorem addressing in a unified manner two important problems in function approximation: avoiding curse of dimensionality and estimating the degree of approximation for out-of-sample extension in manifold learning.
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Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review
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Nonparametric regression using deep neural networks with relu activation function
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