Neural Network Approximation of Refinable Functions-Reference-Cited by-同舟云学术

Neural Network Approximation of Refinable Functions

Published:2023-01 Issue:1 Volume:69 Page:482-495
ISSN:0018-9448
Container-title:IEEE Transactions on Information Theory
language:
Short-container-title:IEEE Trans. Inform. Theory

Author:

Daubechies Ingrid¹,DeVore Ronald²,Dym Nadav³,Faigenbaum-Golovin Shira¹,Kovalsky Shahar Z.⁴,Lin Kung-Chin²,Park Josiah²,Petrova Guergana²,Sober Barak⁵^ORCID

Affiliation:

1. Department of Mathematics, and Rhodes Information Initiative, Duke University, Durham, NC, USA

2. Department of Mathematics, Texas A&M University, College Station, TX, USA

3. Faculty of Mathematics, Technion—Israel Institute of Technology, Haifa, Israel

4. Department of Mathematics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

5. Department of Statistics and Data Science and Digital Humanities, The Hebrew University of Jerusalem, Jerusalem, Israel

Funder

NSF

Office of Naval Research

Tripods

THEORINET Simons

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Link

https://ieeexplore.ieee.org/ielam/18/9997158/9858915-aam.pdf

Reference28 articles.

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3. The power of depth for feedforward neural networks;eldan;Proc Conf Learn Theory,2016

4. Deep Neural Network Approximation Theory

5. Expression of Fractals Through Neural Network Functions

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