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We present a greedy-based approach to construct an efficient single hidden layer neural network with the ReLU activation that approximates a target function.
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Some remarks on greedy algorithms
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Generalized approximate weak greedy algorithms
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Greedy algorithms with restricted depth search
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Convex neural networks
Y. Bengio, N. L. Roux, P. Vincent, O. Delalleau, and P. Marcotte · 2006
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Signal recovery from partial information via orthogonal matching pursuit
J. Tropp, A. C. Gilbert, et al · 2007
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Approximation and learning by greedy algorithms
A. R. Barron, A. Cohen, W. Dahmen, and R. A. DeVore · 2008
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I. Gómez, L. Franco, and J. M. Jerez · 2009
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X. Glorot and Y. Bengio · 2010
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Convergence rates for greedy algorithms in reduced basis methods
P. Binev, A. Cohen, W. Dahmen, R. DeVore, G. Petrova, and P. Wojtaszczyk · 2011
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Breaking the curse of dimensionality with convex neural networks
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Neural network with unbounded activation functions is universal approximator
S. Sonoda and N. Murata · 2017
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Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
M. Soltanolkotabi, A. Javanmard, and J. D. Lee · 2018
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Deep ReLU networks have surprisingly few activation patterns
B. Hanin and D. Rolnick · 2019
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