DUSG-Tomo-Net performs super-resolved gridless TomoSAR inversion by learning a Toeplitz-structured covariance representation from single-look nonuniform-baseline data via deep unfolding and projection enforcement.
and Saunders, Michael A
3 Pith papers cite this work, alongside 6,970 external citations. Polarity classification is still indexing.
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The sharp MSE bound for the ℓ1-minimum-norm interpolator under isotropic Gaussian covariates is recovered via the geometry of symmetric Gaussian polytopes, without the convex Gaussian min-max theorem.
A quantum-inspired global search method called QIEO outperforms traditional solvers in recovering sparse structures and robust fitting by maintaining a broad view of possible solutions.
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DUSG-Tomo-Net: A Deep Unfolded Neural Network for Super-Resolving Gridless Spaceborne SAR Tomography via Learned Toeplitz-Structured Covariance Representation
DUSG-Tomo-Net performs super-resolved gridless TomoSAR inversion by learning a Toeplitz-structured covariance representation from single-look nonuniform-baseline data via deep unfolding and projection enforcement.
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Minimum Norm Interpolation via The Local Theory of Banach Spaces: The Role of Gaussianity
The sharp MSE bound for the ℓ1-minimum-norm interpolator under isotropic Gaussian covariates is recovered via the geometry of symmetric Gaussian polytopes, without the convex Gaussian min-max theorem.
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Exploring the non-convexity in machine learning using quantum-inspired optimization
A quantum-inspired global search method called QIEO outperforms traditional solvers in recovering sparse structures and robust fitting by maintaining a broad view of possible solutions.