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We investigate the expressive power of depth-2 bandlimited random neural networks.
The perceptron: a probabilistic model for information storage and organization in the brain
Rosenblatt, F · 1958
Earlier work this paper cites.
Extensions of lipschitz mappings into a hilbert space
Johnson, W. B. and Lindenstrauss, J · 1984
Earlier work this paper cites.
Capabilities of three-layered perceptrons
Irie, B. and Miyake, S · 1988
Earlier work this paper cites.
Perceptrons
Minsky, M. and Papert, S · 1988
Earlier work this paper cites.
Construction of neural nets using the Radon transform
Carroll, S. M. and Dickinson, B. W · 1989
Earlier work this paper cites.
On the approximate realization of continuous mappings by neural networks
Funahashi, K.-I · 1989
Earlier work this paper cites.
Representation of functions by superpositions of a step or sigmoid function and their applications to neural network theory
Ito, Y · 1991
Earlier work this paper cites.
Feedforward neural networks with random weights
Schmidt, W. F., Kraaijveld, M. A., and Duin, R. P · 1992
Earlier work this paper cites.
Universal approximation bounds for superpositions of a sigmoidal function
Barron, A. R · 1993
Earlier work this paper cites.
Learning and generalization characteristics of the random vector functional-link net
Pao, Y. H., Park, G. H., and Sobajic, D. J · 1994
Earlier work this paper cites.
Stochastic choice of basis functions in adaptive function approximation and the functional-link net
Igelnik, B. and Pao, Y.-H · 1995
Earlier work this paper cites.
An integral representation of functions using three-layered networks and their approximation bounds
Murata, N · 1996
Earlier work this paper cites.
Bayesian Learning for Neural Networks
Neal, R. M · 1996
Earlier work this paper cites.
The Calderón reproducing formula, windowed X-ray transforms, and radon transforms in L p L^{p} -spaces
Rubin, B · 1998
Earlier work this paper cites.
Harmonic analysis of neural networks
Candès, E. J · 1999
Earlier work this paper cites.
Emerging applications of geometric multiscale analysis
Donoho, D. L · 2002
Earlier work this paper cites.
Adaptive nonlinear system identification with echo state networks
Jaeger, H · 2002
Earlier work this paper cites.
Comparison of worst case errors in linear and neural network approximation
Kůrková, V. and Sanguineti, M · 2002
Earlier work this paper cites.
Kernel methods for deep learning
Cho, Y. and Saul, L. K · 2009
Earlier work this paper cites.
Reservoir computing approaches to recurrent neural network training
LukoševičIus, M. and Jaeger, H · 2009
Earlier work this paper cites.
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Mallat, S · 2009
Earlier work this paper cites.
Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning
Rahimi, A. and Recht, B · 2009
Earlier work this paper cites.
The ridgelet and curvelet transforms
Starck, J.-L., Murtagh, F., and Fadili, J. M · 2010
Cited alongside, same era.
On random weights and unsupervised feature learning
Saxe, A. M., Koh, P. W., Chen, Z., Bhand, M., Suresh, B., and Ng, A. Y · 2011
Cited alongside, same era.
Nonlinear system modeling with random matrices: echo state networks revisited
Zhang, B., Miller, D. J., and Wang, Y · 2011
Cited alongside, same era.
A comparison between fixed-basis and variable-basis schemes for function approximation and functional optimization
Gnecco, G · 2012
Cited alongside, same era.
Approximating multivariable functions by feedforward neural nets
Kainen, P. C., Kůrková, V., and Sanguineti, M · 2013
Cited alongside, same era.
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Approximation by Combinations of ReLU and Squared ReLU Ridge Functions with ℓ 1 \ell^{1} and ℓ 0 \ell^{0} Controls
Klusowski, J. M. and Barron, A. R · 2018
Later among the works it cites.
A random matrix approach to neural networks
Louart, C., Liao, Z., Couillet, R., et al · 2018
Later among the works it cites.
The global optimum of shallow neural network is attained by ridgelet transform
Sonoda, S., Ishikawa, I., Ikeda, M., Hagihara, K., Sawano, Y., Matsubara, T., and Murata, N · 2018
Later among the works it cites.
Fast generalization error bound of deep learning from a kernel perspective
Suzuki, T · 2018
Later among the works it cites.
Reconciling modern machine learning practice and the classical bias–variance trade-off
Belkin, M., Hsu, D., Ma, S., and Mandal, S · 2019
Later among the works it cites.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Later among the works it cites.
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Fast approximation and estimation bounds of kernel quadrature for infinitely wide models
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