Gradient Descent on Point Clouds and Applications in Learned Operator Correction | Paper Citation Record

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This paper cites Absil, R.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Absil, R

Reference 1

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This paper cites Posterior contraction rates for the Bayesian approach to linear ill-posed inverse problems.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Posterior contraction rates for the Bayesian approach to linear ill-posed inverse problems

Reference 2

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This paper cites Approximation errors and model reduction with an application in optical diffusion tomography.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Approximation errors and model reduction with an application in optical diffusion tomography

Reference 3

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This paper cites Inverse Problems with Learned Forward Operators.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Inverse Problems with Learned Forward Operators

Reference 4

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This paper cites Data driven regularization by projection.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Data driven regularization by projection

Reference 5

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This paper cites Biomedical photoacoustic imaging.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Biomedical photoacoustic imaging

Reference 6

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This paper cites Modern Regularization Methods for Inverse Problems.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Modern Regularization Methods for Inverse Problems

Reference 7

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This paper cites Consistency and rates of convergence of nonlinear Tikhonov regularization with random noise.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Consistency and rates of convergence of nonlinear Tikhonov regularization with random noise

Reference 8

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This paper cites Manifold reconstruction using tangential Delaunay complexes.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Manifold reconstruction using tangential Delaunay complexes

Reference 9

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Gradient Descent on Point Clouds and Applications in Learned Operator Correction Stochastic gradient descent on Riemannian manifolds

Reference 10

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This paper cites Brezis and H.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Brezis and H

Reference 11

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This paper cites Asymptotic equivalence of nonparametric regression and white noise.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Asymptotic equivalence of nonparametric regression and white noise

Reference 12

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This paper cites Discrete varifolds and surface approximation.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Discrete varifolds and surface approximation

Reference 13

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This paper cites Discrete varifolds: a unified framework for discrete approxima- tions of surfaces and mean curvature.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Discrete varifolds: a unified framework for discrete approxima- tions of surfaces and mean curvature

Reference 14

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Gradient Descent on Point Clouds and Applications in Learned Operator Correction A varifold approach to surface approximation

Reference 15

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This paper cites Variational regularisation for inverse prob- lems with imperfect forward operators and general noise models.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Variational regularisation for inverse prob- lems with imperfect forward operators and general noise models

Reference 16

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This paper cites Burago, Y.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Burago, Y

Reference 17

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This paper cites A graph discretization of the Laplace–Beltrami operator.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction A graph discretization of the Laplace–Beltrami operator

Reference 18

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This paper cites Dynamic updating of numerical model discrepancy using sequential sampling.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Dynamic updating of numerical model discrepancy using sequential sampling

Reference 19

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This paper cites Smooth manifold reconstruction from noisy and non-uniform approximation with guarantees.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Smooth manifold reconstruction from noisy and non-uniform approximation with guarantees

Reference 20

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This paper cites Manifold reconstruction from point samples.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Manifold reconstruction from point samples

Reference 21

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This paper cites Fast calculation of pulsed photoacoustic fields in fluids using k-space methods.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Fast calculation of pulsed photoacoustic fields in fluids using k-space methods

Reference 22

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Gradient Descent on Point Clouds and Applications in Learned Operator Correction Unresolved cited work

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This paper cites Provable surface reconstruction from noisy samples.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Provable surface reconstruction from noisy samples

Reference 24

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This paper cites Iso-Riemannian Optimization on Learned Data Manifolds.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Iso-Riemannian Optimization on Learned Data Manifolds

Reference 25

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Gradient Descent on Point Clouds and Applications in Learned Operator Correction Fixing nonconvergence of algebraic iterative reconstruction with an unmatched backprojector

Reference 26

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This paper cites Curvature measures.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Curvature measures

Reference 27

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Gradient Descent on Point Clouds and Applications in Learned Operator Correction Minimax manifold estimation

Reference 28

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This paper cites Adaptive estimation of linear functionals in Hilbert scales from indirect white noise observations.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Adaptive estimation of linear functionals in Hilbert scales from indirect white noise observations

Reference 29

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This paper cites Approximate k-space models and deep learning for fast photoacoustic reconstruc- tion.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Approximate k-space models and deep learning for fast photoacoustic reconstruc- tion

Reference 30

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Gradient Descent on Point Clouds and Applications in Learned Operator Correction Projected gradient descent on Riemannian manifolds with applications to online power system optimization

Reference 31

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This paper cites Convergence Rates for Learning Linear Operators from Noisy Data.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Convergence Rates for Learning Linear Operators from Noisy Data

Reference 32

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This paper cites Kaipio and E.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Kaipio and E

Reference 33

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Gradient Descent on Point Clouds and Applications in Learned Operator Correction Statistical inverse problems: discretization, model reduction and inverse crimes

Reference 34

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This paper cites Bayesian calibration of computer models.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Bayesian calibration of computer models

Reference 35

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This paper cites Temporal backward projection of optoacoustic pressure transients using Fourier transform methods.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Temporal backward projection of optoacoustic pressure transients using Fourier transform methods

Reference 36

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This paper cites Fourier neural operator for parametric partial differential equations.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Fourier neural operator for parametric partial differential equations

Reference 37

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This paper cites Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Multiscale geometric methods for data sets I: Multiscale SVD, noise and curvature

Reference 38

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This paper cites On Learned Operator Correction in Inverse Problems.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction On Learned Operator Correction in Inverse Problems

Reference 39

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This paper cites A topological view of unsupervised learning from noisy data.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction A topological view of unsupervised learning from noisy data

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:08:17.394339Z

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This paper cites Computed Tomography Reconstruction with Uncertain View Angles by Iteratively Updated Model Discrepancy.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Computed Tomography Reconstruction with Uncertain View Angles by Iteratively Updated Model Discrepancy

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:08:17.378380Z

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This paper cites A gradient-descent method for curve fitting on Riemannian manifolds.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction A gradient-descent method for curve fitting on Riemannian manifolds

Reference 42

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This paper cites Optimization Techniques on Riemannian Manifolds.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Optimization Techniques on Riemannian Manifolds

Reference 43

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Gradient Descent on Point Clouds and Applications in Learned Operator Correction Unresolved cited work

Reference 44

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This paper cites k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:08:17.332887Z

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This paper cites Modeling nonlinear ultrasound propagation in heterogeneous media with power law absorption using ak-space pseudospectral method.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Modeling nonlinear ultrasound propagation in heterogeneous media with power law absorption using ak-space pseudospectral method

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:08:17.201824Z

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Observation 58405b2c-effa-40e7-b893-9436f8297e82 · outbound

This paper cites Tangent space estimation for smooth embeddings of riemannian manifolds®.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Tangent space estimation for smooth embeddings of riemannian manifolds®

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:08:17.186729Z

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Observation 2a38b4a0-373a-4b9c-8231-3f9837edb852 · outbound

This paper cites Wahba.Spline models for observational data.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction Wahba.Spline models for observational data

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:08:17.169869Z

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Observation e2df96d9-e34a-417f-9106-f1e59b6c0ee5 · outbound

This paper cites A useful variant of the Davis–Kahan theorem for statisticians.

Gradient Descent on Point Clouds and Applications in Learned Operator Correction A useful variant of the Davis–Kahan theorem for statisticians

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:08:17.154862Z

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