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Consider the multivariate nonparametric regression model.
The state of sparsity in deep neural networks
Gale, T., Elsen, E., and Hooker, S · 1902
Earlier work this paper cites.
How do infinite width bounded norm networks look in function space?
Savarese, P., Evron, I., Soudry, D., and Srebro, N · 1902
Earlier work this paper cites.
How can we be so dense? The benefits of using highly sparse representations
Ahmad, S., and Scheinkman, L · 1903
Earlier work this paper cites.
Weight agnostic neural networks
Gaier, A., and Ha, D · 1906
Earlier work this paper cites.
Adaptive approximation and estimation of deep neural network with intrinsic dimensionality
Nakada, R., and Imaizumi, M · 1907
Earlier work this paper cites.
Deep ReLU network approximation of functions on a manifold
Schmidt-Hieber, J · 1908
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 1958
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
Cybenko, G · 1989
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators
Hornik, K., Stinchcombe, M., and White, H · 1989
Earlier work this paper cites.
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Hornik, K., Stinchcombe, M., and White, H · 1990
Earlier work this paper cites.
Optimal brain damage
LeCun, Y., Denker, J. S., and Solla, S. A · 1990
Earlier work this paper cites.
Universal approximation bounds for superpositions of a sigmoidal function
Barron, A. R · 1993
Earlier work this paper cites.
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Cohen, A., Daubechies, I., and Vial, P · 1993
Earlier work this paper cites.
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Earlier work this paper cites.
Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
Leshno, M., Lin, V. Y., Pinkus, A., and Schocken, S · 1993
Earlier work this paper cites.
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Mhaskar, H. N · 1993
Earlier work this paper cites.
Approximation and estimation bounds for artificial neural networks
Barron, A. R · 1994
Earlier work this paper cites.
Nonparametric regression and generalized linear models
Green, P. J., and Silverman, B. W · 1994
Earlier work this paper cites.
Convergence rates for single hidden layer feedforward networks
McCaffrey, D. F., and Gallant, A. R · 1994
Earlier work this paper cites.
Weak convergence and empirical processes
van der Vaart, A. W., and Wellner, J. A · 1996
Earlier work this paper cites.
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Scarselli, F., and Tsoi, A. C · 1998
Earlier work this paper cites.
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Anthony, M., and Bartlett, P. L · 1999
Earlier work this paper cites.
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Pinkus, A · 1999
Earlier work this paper cites.
Neural network approximation of continuous functionals and continuous functions on compactifications
Stinchcombe, M · 1999
Earlier work this paper cites.
New ties between computational harmonic analysis and approximation theory
Candès, Emmanuel J · 2002
Earlier work this paper cites.
A distribution-free theory of nonparametric regression
Györfi, L., Kohler, M., Krzyżak, A., and Walk, H · 2002
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