A unified Fourier slice method to derive ridgelet transform for a variety of depth-2 neural networks-Reference-Cited by-同舟云学术

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A unified Fourier slice method to derive ridgelet transform for a variety of depth-2 neural networks

Published:2024-12 Issue: Volume:233 Page:106184
ISSN:0378-3758
Container-title:Journal of Statistical Planning and Inference
language:en
Short-container-title:Journal of Statistical Planning and Inference

Author:

Sonoda Sho^ORCID,Ishikawa Isao,Ikeda Masahiro

Funder

Japan Society for the Promotion of Science

Japan Science and Technology Agency

Publisher

Elsevier BV

Reference66 articles.

1. Universal approximation bounds for superpositions of a sigmoidal function;Barron;IEEE Trans. Inform. Theory,1993

2. Unitarization of the horocyclic Radon transform on symmetric spaces;Bartolucci,2021

3. Convex neural networks;Bengio;Adv. Neural Inf. Process. Syst.,2006

4. Geometric deep learning: Grids, groups, graphs, geodesics, and gauges;Bronstein,2021

5. Invariant scattering convolution networks;Bruna;IEEE Trans. Pattern Anal. Mach. Intell.,2013

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