A non-asymptotic bound on compression error for signal parameterizations derived from differences in predictions at varying compression levels, verified empirically across fitting and inverse problems.
Universal approximation bounds for superpositions of a sigmoidal function
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Tikhonov regularization is analyzed using neural operators as learned surrogates for ill-posed nonlinear operator equations, with error balancing and approximation results extended to Sobolev and Lebesgue spaces.
citing papers explorer
-
Bounding Global and Local Compression Error of Signal Parameterizations
A non-asymptotic bound on compression error for signal parameterizations derived from differences in predictions at varying compression levels, verified empirically across fitting and inverse problems.
-
Neural operators for solving nonlinear inverse problems
Tikhonov regularization is analyzed using neural operators as learned surrogates for ill-posed nonlinear operator equations, with error balancing and approximation results extended to Sobolev and Lebesgue spaces.