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Hrushikesh Mhaskar
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Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review
T Poggio, H Mhaskar, L Rosasco, B Miranda, Q Liao
International Journal of Automation and Computing 14 (5), 503-519, 2017
4782017
Neural networks for optimal approximation of smooth and analytic functions
HN Mhaskar
Neural computation 8 (1), 164-177, 1996
3731996
Approximation by superposition of sigmoidal and radial basis functions
HN Mhaskar, CA Micchelli
Advances in Applied mathematics 13 (3), 350-373, 1992
3461992
Deep vs. shallow networks: An approximation theory perspective
HN Mhaskar, T Poggio
Analysis and Applications 14 (06), 829-848, 2016
3062016
Where does the sup norm of a weighted polynomial live?
HN Mhaskar, EB Saff
Constructive Approximation 1 (1), 71-91, 1985
2711985
Extremal problems for polynomials with exponential weights
HN Mhaskar, EB Saff
Transactions of the American Mathematical Society 285 (1), 203-234, 1984
2341984
Spherical Marcinkiewicz-Zygmund inequalities and positive quadrature
H Mhaskar, F Narcowich, J Ward
Mathematics of computation 70 (235), 1113-1130, 2001
2252001
Introduction to the theory of weighted polynomial approximation
HN Mhaskar
World Scientific, 1996
2021996
Approximation properties of a multilayered feedforward artificial neural network
HN Mhaskar
Advances in Computational Mathematics 1 (1), 61-80, 1993
1691993
When and why are deep networks better than shallow ones?
H Mhaskar, Q Liao, T Poggio
Proceedings of the AAAI conference on artificial intelligence 31 (1), 2017
1662017
Degree of approximation by neural and translation networks with a single hidden layer
HN Mhaskar, CA Micchelli
Advances in applied mathematics 16 (2), 151-183, 1995
1611995
Learning functions: when is deep better than shallow
H Mhaskar, Q Liao, T Poggio
arXiv preprint arXiv:1603.00988, 2016
1422016
Fundamentals of approximation theory
HN Mhaskar, DV Pai
CRC Press, 2000
1212000
On trigonometric wavelets
CK Chui, HN Mhaskar
Constructive Approximation 9 (2), 167-190, 1993
1181993
A proof of Freud's conjecture for exponential weights
DS Lubinsky, HN Mhaskar, EB Saff
Constructive Approximation 4 (1), 65-83, 1988
1141988
Neural networks for localized approximation
CK Chui, X Li, HN Mhaskar
mathematics of computation 63 (208), 607-623, 1994
1121994
Theory of deep learning III: explaining the non-overfitting puzzle
T Poggio, K Kawaguchi, Q Liao, B Miranda, L Rosasco, X Boix, J Hidary, ...
arXiv preprint arXiv:1801.00173, 2017
1002017
Dimension-independent bounds on the degree of approximation by neural networks
HN Mhaskar, CA Micchelli
IBM Journal of Research and Development 38 (3), 277-284, 1994
941994
Diffusion polynomial frames on metric measure spaces
M Maggioni, HN Mhaskar
Applied and Computational Harmonic Analysis 24 (3), 329-353, 2008
932008
A deep learning approach to diabetic blood glucose prediction
HN Mhaskar, SV Pereverzyev, MD Van der Walt
Frontiers in Applied Mathematics and Statistics 3, 14, 2017
862017
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