Gabor neural networks with proven approximation properties
From MaRDI portal
Publication:2050710
DOI10.1007/s12220-020-00575-zOpenAlexW3119949288WikidataQ114221041 ScholiaQ114221041MaRDI QIDQ2050710
Publication date: 31 August 2021
Published in: The Journal of Geometric Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s12220-020-00575-z
Artificial neural networks and deep learning (68T07) Trigonometric approximation (42A10) General harmonic expansions, frames (42C15)
Cites Work
- Unnamed Item
- Continuity properties of the Gabor frame operator
- A unified characterization of reproducing systems generated by a finite family. II
- Oversampling, quasi-affine frames, and wave packets
- Foundations of time-frequency analysis
- A unified characterization of reproducing systems generated by a finite family
- Provable approximation properties for deep neural networks
- Discrete directional Gabor frames
- Rotationally invariant time-frequency scattering transforms
- Regularity of dual Gabor windows
- Analysis of time-frequency scattering transforms
- Group Invariant Scattering
- Continuous and Discrete Reproducing Systems That Arise from Translations. Theory and Applications of Composite Wavelets
- Wilson Bases and Modulation Spaces
- Universal approximation bounds for superpositions of a sigmoidal function
- A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction
- Lipschitz properties for deep convolutional networks
- Deep Neural Network Approximation Theory
- Optimal Approximation with Sparsely Connected Deep Neural Networks
- A Class of Nonharmonic Fourier Series
- Approximation by superpositions of a sigmoidal function
- Weyl-Heisenberg frames, translation invariant systems and the Walnut representation
This page was built for publication: Gabor neural networks with proven approximation properties
