Paper Citation Record · LEDGER

Shallow neural network yields regularization for ill-posed inverse problems

As of 6 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2511.16171.

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2511.16171 v2

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measured 58 of 58 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Source: pith, observed 2026-06-28T18:42:29.615621Z

Reference resolution

58 of 58 outbound references displayed

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Outbound references

Observation 5e9cf2cd-7fc4-42f0-892c-f1a22936a9f9 · outbound

This paper cites an unresolved cited work.

Shallow neural network yields regularization for ill-posed inverse problems Unresolved cited work

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Observation 65f9aaf9-0cec-4afc-a63a-9383bdb13703 · outbound

This paper cites Approximation by superpositions of a sigmoidal function,.

Shallow neural network yields regularization for ill-posed inverse problems Approximation by superpositions of a sigmoidal function,

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This paper cites Multilayer feedforward networks are universal approximators,.

Shallow neural network yields regularization for ill-posed inverse problems Multilayer feedforward networks are universal approximators,

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Shallow neural network yields regularization for ill-posed inverse problems Approximation capabilities of multilayer feedforward networks,

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This paper cites Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions,.

Shallow neural network yields regularization for ill-posed inverse problems Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions,

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Shallow neural network yields regularization for ill-posed inverse problems Universal approximation bounds for superpositions of a sigmoidal function,

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Shallow neural network yields regularization for ill-posed inverse problems Benefits of depth in neural networks,

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Shallow neural network yields regularization for ill-posed inverse problems The power of depth for feedforward neural networks,

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Shallow neural network yields regularization for ill-posed inverse problems Deep network approximation for smooth functions,

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Shallow neural network yields regularization for ill-posed inverse problems Optimal approximation rate of ReLU networks in terms of width and depth,

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Shallow neural network yields regularization for ill-posed inverse problems Deep network approximation characterized by number of neurons,

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Shallow neural network yields regularization for ill-posed inverse problems Deep network approximation: Beyond RELU to diverse activation functions,

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Shallow neural network yields regularization for ill-posed inverse problems ReLU network with widthd+O(1)can achieve optimal approximation rate,

Reference 13

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Shallow neural network yields regularization for ill-posed inverse problems The phase diagram of approximation rates for deep neural networks,

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Shallow neural network yields regularization for ill-posed inverse problems Simultaneous neural network approximation for smooth functions,

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Shallow neural network yields regularization for ill-posed inverse problems The deep Ritz method: a deep learning-based numerical algorithm for solving variational problems,

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Shallow neural network yields regularization for ill-posed inverse problems DGM: A deep learning algorithm for solving partial differential equations,

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This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,.

Shallow neural network yields regularization for ill-posed inverse problems Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

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Shallow neural network yields regularization for ill-posed inverse problems Weak adversarial networks for high-dimensional partial differential equations,

Reference 19

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Shallow neural network yields regularization for ill-posed inverse problems Generative adversarial network: An overview of theory and applications,

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Shallow neural network yields regularization for ill-posed inverse problems KAN: Kolmogorov–arnold networks,

Reference 21

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Shallow neural network yields regularization for ill-posed inverse problems Extensions of the deep galerkin method,

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Shallow neural network yields regularization for ill-posed inverse problems Deep convolutional ritz method: parametric PDE surrogates without labeled data,

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Shallow neural network yields regularization for ill-posed inverse problems A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics,

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Shallow neural network yields regularization for ill-posed inverse problems A framework for data-driven solution and parameter estimation of pdes using conditional generative adversarial networks,

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Shallow neural network yields regularization for ill-posed inverse problems Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations,

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Shallow neural network yields regularization for ill-posed inverse problems Kolmogorov–Arnold-Informed neural network: A physics- informed deep learning framework for solving forward and inverse problems based on Kolmogorov–Arnold Networks,

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Shallow neural network yields regularization for ill-posed inverse problems Gradient-based learning applied to document recognition,

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Shallow neural network yields regularization for ill-posed inverse problems U-net: Convolutional networks for biomedical image segmentation,

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Shallow neural network yields regularization for ill-posed inverse problems Numerical solution of inverse problems by weak adversarial networks,

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Shallow neural network yields regularization for ill-posed inverse problems Electrical impedance tomography with deep calder ´on method,

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Shallow neural network yields regularization for ill-posed inverse problems Error-constraint deep learning scheme for electrical impedance tomography (EIT),

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Shallow neural network yields regularization for ill-posed inverse problems Solving ill-posed inverse problems using iterative deep neural networks,

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Shallow neural network yields regularization for ill-posed inverse problems Unsupervised knowledge-transfer for learned image reconstruction,

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Shallow neural network yields regularization for ill-posed inverse problems Deep convolutional neural network for inverse problems in imaging,

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Shallow neural network yields regularization for ill-posed inverse problems NETT: Solving inverse problems with deep neural networks,

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Shallow neural network yields regularization for ill-posed inverse problems Deep unfolding as iterative regularization for imaging inverse problems,

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Shallow neural network yields regularization for ill-posed inverse problems Learning a variational network for reconstruction of accelerated MRI data,

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Shallow neural network yields regularization for ill-posed inverse problems A deep cascade of convolutional neural networks for dynamic MR image reconstruction,

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Shallow neural network yields regularization for ill-posed inverse problems Unresolved cited work

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Shallow neural network yields regularization for ill-posed inverse problems Kaltenbacher, A

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Shallow neural network yields regularization for ill-posed inverse problems Data errors and an error estimation for ill-posed problems,

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Shallow neural network yields regularization for ill-posed inverse problems The method of extending compacts and a posteriori error estimates for nonlinear ill-posed problems,

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Shallow neural network yields regularization for ill-posed inverse problems A coupled complex boundary expanding compacts method for inverse source problems,

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Shallow neural network yields regularization for ill-posed inverse problems The Barron Space and the Flow-induced Function Spaces for Neural Network Models

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Shallow neural network yields regularization for ill-posed inverse problems Two-layer networks with the ReLU k activation function: Barron spaces and derivative approximation,

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Shallow neural network yields regularization for ill-posed inverse problems Saturation of regularization methods for linear ill-posed problems in Hilbert spaces,

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Shallow neural network yields regularization for ill-posed inverse problems On the second order asymptotical regularization of linear ill-posed inverse problems,

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Shallow neural network yields regularization for ill-posed inverse problems A scaling fractional asymptotical regularization method for linear inverse problems,

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Shallow neural network yields regularization for ill-posed inverse problems Approximate source conditions for nonlinear ill-posed problems – chances and limitations,

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Shallow neural network yields regularization for ill-posed inverse problems A convergence rates result for Tikhonov regularization in Banach spaces with non-smooth operators,

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Shallow neural network yields regularization for ill-posed inverse problems Flemming,Variational Source Conditions Yield Convergence Rates

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Shallow neural network yields regularization for ill-posed inverse problems Deautoconvolution in the two-dimensional case,

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Shallow neural network yields regularization for ill-posed inverse problems On the autoconvolution equation and total variation constraints,

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Shallow neural network yields regularization for ill-posed inverse problems Calder ´on’s inverse conductivity problem in the plane,

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Shallow neural network yields regularization for ill-posed inverse problems Singular solutions of elliptic equations and the determination of conductivity by boundary measurements,

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Shallow neural network yields regularization for ill-posed inverse problems Barron Space for Graph Convolution Neural Networks

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Deep neural network yields regularization for ill-posed inverse problems cites this paper.

Deep neural network yields regularization for ill-posed inverse problems Shallow neural network yields regularization for ill-posed inverse problems

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