Recovery of Surfaces and Functions in High Dimensions: Sampling Theory and Links to Neural Networks
DOI10.1137/20M1340654zbMath1479.94093arXiv2005.12860OpenAlexW3160806898MaRDI QIDQ5860295
Publication date: 19 November 2021
Published in: SIAM Journal on Imaging Sciences (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2005.12860
Artificial neural networks and deep learning (68T07) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Numerical methods for trigonometric approximation and interpolation (65T40) Fourier series and coefficients in several variables (42B05) Sampling theory in information and communication theory (94A20)
Uses Software
Cites Work
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- Trigonometric interpolation on lattice grids
- Kernel methods in system identification, machine learning and function estimation: a survey
- Single-machine and two-machine flowshop scheduling with general learning functions
- Fourier reconstruction of functions from their nonstandard sampled Radon transform
- Geometric measure theory.
- Off-the-Grid Recovery of Piecewise Constant Images from Few Fourier Samples
- Low-rank Matrix Recovery via Iteratively Reweighted Least Squares Minimization
- Radial basis functions and level set method for structural topology optimization
- A Theory for Sampling Signals From a Union of Subspaces
- Time-Delay Estimation From Low-Rate Samples: A Union of Subspaces Approach
- Xampling: Signal Acquisition and Processing in Union of Subspaces
- Sampling Curves With Finite Rate of Innovation
- Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
- Sampling of Planar Curves: Theory and Fast Algorithms
- Learning Theory and Kernel Machines
- Variational B-Spline Level-Set: A Linear Filtering Approach for Fast Deformable Model Evolution
- Distance Regularized Level Set Evolution and Its Application to Image Segmentation
- Learning Doubly Sparse Transforms for Images
- Summability of Multi-Dimensional Trigonometric Fourier Series
- Testing the manifold hypothesis
- Level set methods: An overview and some recent results
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