Rotationally invariant time-frequency scattering transforms

DOI10.1007/s00041-019-09705-wzbMath1437.42047arXiv1710.06889OpenAlexW3000355694MaRDI QIDQ2288432

Wojciech Czaja, Weilin Li

Publication date: 17 January 2020

Published in: The Journal of Fourier Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1710.06889

zbMATH Keywords

neural networksfeature extractionscattering transformtime-frequencydirectional representationsuniform covering frames

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10) General harmonic expansions, frames (42C15) Miscellaneous applications of operator theory (47N99)

Related Items (3)

Central and Noncentral Limit Theorems Arising from the Scattering Transform and Its Neural Activation Generalization ⋮ Analysis of time-frequency scattering transforms ⋮ Gabor neural networks with proven approximation properties

Uses Software

• ScatNet

Cites Work

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• Gabor frames and directional time-frequency analysis
• Harmonic analysis of neural networks
• Discrete directional Gabor frames
• Graph convolutional neural networks via scattering
• Analysis of time-frequency scattering transforms
• Group Invariant Scattering
• Isotropic Shearlet Analogs forL²(ℝ^k) and Localization Operators
• New tight frames of curvelets and optimal representations of objects with piecewise C² singularities
• A Mathematical Theory of Deep Convolutional Neural Networks for Feature Extraction
• Deep Scattering Spectrum
• Deep Haar scattering networks
• Anisotropic shearlet transforms for L²
• Optimally Sparse Multidimensional Representation Using Shearlets
• Wavelet Scattering Regression of Quantum Chemical Energies

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