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In this effort, we derive a formula for the integral representation of a shallow neural network with the ReLU activation function.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, Ronald J Williams, et al · 1988
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Representation of functions by superpositions of a step or sigmoid function and their applications to neural network theory
Yoshifusa Ito · 1991
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Universal approximation bounds for superpositions of a sigmoidal function
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Learning and generalization characteristics of the random vector functional-link net
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Regularization of inverse problems , volume 375
Heinz Werner Engl, Martin Hanke, and Andreas Neubauer · 1996
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Věra Kůrková, Paul C Kainen, and Vladik Kreinovich · 1997
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Francis Bach · 2017
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Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review
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Neural network with unbounded activation functions is universal approximator
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Barron spaces and the compositional function spaces for neural network models
Chao Ma, Lei Wu, et al · 2019
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