Orthogonal greedy algorithm for linear operator learning with shallow neural network | Paper Citation Record

Observation 65679ca3-ddff-45e1-ba7e-34a2ecd62888 · outbound

This paper cites Physics-informed machine learning.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Physics-informed machine learning

Reference 1

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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.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 2

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This paper cites Dgm: A deep learning algorithm for solving partial di fferential equations.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Dgm: A deep learning algorithm for solving partial di fferential equations

Reference 3

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Observation 05c0a083-5ee8-4004-9970-b1cc1dcd5b52 · outbound

This paper cites The deep ritz method: a deep learning-based numerical algorithm for solving variational problems.

Orthogonal greedy algorithm for linear operator learning with shallow neural network The deep ritz method: a deep learning-based numerical algorithm for solving variational problems

Reference 4

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This paper cites Data-driven discovery of green’s functions with human-understandable deep learning.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Data-driven discovery of green’s functions with human-understandable deep learning

Reference 5

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Observation 30c55e55-6571-42b9-98bc-22d7c309b5a0 · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

Reference 6

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This paper cites Stuart, and Anima Anandkumar.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Stuart, and Anima Anandkumar

Reference 7

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Observation f823c40d-ab5f-4a24-aa26-1822ebc380c1 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Adam: A Method for Stochastic Optimization

Reference 8

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This paper cites Practical methods of optimization.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Practical methods of optimization

Reference 9

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Observation 2eb3ce72-7e32-4c0e-a09f-8289c821f3a1 · outbound

This paper cites Elliptic pde learning is provably data-e fficient.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Elliptic pde learning is provably data-e fficient

Reference 10

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Observation fd88035d-56ae-4def-a467-390319f3233c · outbound

This paper cites Approximation with Random Shallow ReLU Networks with Applications to Model Reference Adaptive Control.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Approximation with Random Shallow ReLU Networks with Applications to Model Reference Adaptive Control

Reference 12

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Observation 641f367d-6bc2-4fa8-9717-6e3030d1fdcb · outbound

This paper cites Random features for large-scale kernel machines.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Random features for large-scale kernel machines

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:16.098639Z

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Observation 9331d8f0-5dc7-4fe7-8450-c64578729cab · outbound

This paper cites A comparative analysis of optimization and generalization properties of two-layer neural network and random feature models under gradient descent dynamics.

Orthogonal greedy algorithm for linear operator learning with shallow neural network A comparative analysis of optimization and generalization properties of two-layer neural network and random feature models under gradient descent dynamics

Reference 14

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Observation 56d73a37-be1c-46c1-b24e-881ccf3963c6 · outbound

This paper cites Transferable neural networks for partial di fferential equations.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Transferable neural networks for partial di fferential equations

Reference 15

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Observation 4488891f-18af-45af-9a89-c3a285b5a700 · outbound

This paper cites Local extreme learning machines and domain decomposition for solving linear and nonlinear partial di ffer- ential equations.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Local extreme learning machines and domain decomposition for solving linear and nonlinear partial di ffer- ential equations

Reference 16

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Observation fe225a20-c2c5-4ea6-b82b-d81a1358a454 · outbound

This paper cites Extreme learning machine: Theory and applications.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Extreme learning machine: Theory and applications

Reference 17

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Observation 0ba103a0-3bf0-44da-9e67-561b67c215c0 · outbound

This paper cites A Nonoverlapping Domain Decomposition Method for Extreme Learning Machines: Elliptic Problems.

Orthogonal greedy algorithm for linear operator learning with shallow neural network A Nonoverlapping Domain Decomposition Method for Extreme Learning Machines: Elliptic Problems

Reference 18

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Observation ee9c5238-7d69-4138-ad46-81d500ab0e50 · outbound

This paper cites Finite neuron method and convergence analysis.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Finite neuron method and convergence analysis

Reference 19

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Observation ae48db0f-31c5-4fc5-bff6-c89dc63e5619 · outbound

This paper cites A neuron-wise subspace correction method for the finite neuron method.

Orthogonal greedy algorithm for linear operator learning with shallow neural network A neuron-wise subspace correction method for the finite neuron method

Reference 20

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Observation 4bf9722d-92a0-4dc3-aa81-df17c466c7db · outbound

This paper cites Barron, Albert Cohen, Wolfgang Dahmen, and Ronald A.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Barron, Albert Cohen, Wolfgang Dahmen, and Ronald A

Reference 21

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This paper cites an unresolved cited work.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Unresolved cited work

Reference 22

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Observation f1f13759-c318-4ca8-9f08-209aa86b2d85 · outbound

This paper cites Weak greedy algorithms.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Weak greedy algorithms

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.969292Z

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Observation 56b1e21b-0458-4dc4-8320-dce3b42310df · outbound

This paper cites Greedy training algorithms for neural networks and applications to pdes.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Greedy training algorithms for neural networks and applications to pdes

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.953766Z

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Observation 6d25dc57-868a-452c-927d-704c6fda19e3 · outbound

This paper cites Randomized Greedy Algorithms for Neural Network Optimization.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Randomized Greedy Algorithms for Neural Network Optimization

Reference 27

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Observation 86f37697-26f8-4b12-bb24-3d0804e01cd4 · outbound

This paper cites Sharp bounds on the approximation rates, metric entropy, and n-widths of shallow neural networks.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Sharp bounds on the approximation rates, metric entropy, and n-widths of shallow neural networks

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.925562Z

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Observation 39a429b9-87a4-4a95-b362-c41c4a92dfd7 · outbound

This paper cites Mionet: Learning multiple-input operators via tensor product.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Mionet: Learning multiple-input operators via tensor product

Reference 29

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Observation 122921a9-bb35-4c8f-a6ce-f9da4779eba4 · outbound

This paper cites Learning the solution operator of parametric partial di fferential equations with physics- informed DeepONets.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Learning the solution operator of parametric partial di fferential equations with physics- informed DeepONets

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.901493Z

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Observation 0fc96c22-9db0-4568-bd49-93ed9f2ada3a · outbound

This paper cites Improved architectures and training algorithms for deep operator networks.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Improved architectures and training algorithms for deep operator networks

Reference 31

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Observation 36fad25d-0d9e-45e7-b2cd-7eeaaebe15df · outbound

This paper cites Adaptive operator learning for infinite-dimensional bayesian inverse problems.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Adaptive operator learning for infinite-dimensional bayesian inverse problems

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.876758Z

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Observation a8764740-ab4b-46d0-98da-aea158d545a6 · outbound

This paper cites Ib-uq: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Ib-uq: Information bottleneck based uncertainty quantification for neural function regression and neural operator learning

Reference 33

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Observation b8d8994e-2f6c-49fe-9496-3ae9bd7de3dc · outbound

This paper cites Neural operator: Learning maps between function spaces with applications to pdes.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Neural operator: Learning maps between function spaces with applications to pdes

Reference 34

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This paper cites Multipole graph neural operator for parametric partial differential equations.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Multipole graph neural operator for parametric partial differential equations

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.840127Z

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Observation 8c8984f7-bb3a-4736-a7cc-fc8c8f75f268 · outbound

This paper cites Neural Operator: Graph Kernel Network for Partial Differential Equations.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Neural Operator: Graph Kernel Network for Partial Differential Equations

Reference 36

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Observation a9e80a9c-2942-412c-9ce7-0a5e7b209056 · outbound

This paper cites Fourier neural operator with learned deformations for pdes on general geometries.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Fourier neural operator with learned deformations for pdes on general geometries

Reference 37

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Observation 627a3cdf-6751-4951-8c96-4506c8da4e5b · outbound

This paper cites U-NO: U-shaped neural operators.

Orthogonal greedy algorithm for linear operator learning with shallow neural network U-NO: U-shaped neural operators

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.817473Z

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Observation 9f928742-2ed2-449f-9d19-dd8bf655dae1 · outbound

This paper cites Factorized fourier neural operators.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Factorized fourier neural operators

Reference 39

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This paper cites Learning deep implicit fourier neural operators (ifnos) with applications to heterogeneous material modeling.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Learning deep implicit fourier neural operators (ifnos) with applications to heterogeneous material modeling

Reference 40

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Observation a4262519-5685-49ca-8208-9598ec344ff0 · outbound

This paper cites Choose a transformer: Fourier or galerkin.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Choose a transformer: Fourier or galerkin

Reference 41

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This paper cites Transformer meets boundary value inverse problems.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Transformer meets boundary value inverse problems

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.775075Z

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Observation 72a3c756-59c1-4be6-bc92-a49283457d4d · outbound

This paper cites Gnot: A general neural operator transformer for operator learning.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Gnot: A general neural operator transformer for operator learning

Reference 43

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This paper cites Learning operators with coupled attention.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Learning operators with coupled attention

Reference 44

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Observation 61b7c3ef-1156-4be4-93c1-5bf9f9662524 · outbound

This paper cites Mesh-independent operator learning for partial di fferential equations.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Mesh-independent operator learning for partial di fferential equations

Reference 45

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Observation ca189c0c-003f-45fb-acf5-6c567a2e361a · outbound

This paper cites Scalable transformer for pde surrogate modeling.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Scalable transformer for pde surrogate modeling

Reference 46

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Observation 17e49cba-8537-490c-b375-05734521322b · outbound

This paper cites Mgnet: A unified framework of multigrid and convolutional neural network.Science china mathematics, 62:1331– 1354, 2019.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Mgnet: A unified framework of multigrid and convolutional neural network.Science china mathematics, 62:1331– 1354, 2019

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.719360Z

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Observation e2e6e1df-1bec-40aa-8c04-0822fe4a6674 · outbound

This paper cites MgNO: E fficient parameterization of linear operators via multigrid.

Orthogonal greedy algorithm for linear operator learning with shallow neural network MgNO: E fficient parameterization of linear operators via multigrid

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.705441Z

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Observation a3d30c33-4753-46c0-a69e-7eeb16ffb801 · outbound

This paper cites An enhanced v-cycle mgnet model for operator learning in numerical partial di fferential equations.

Orthogonal greedy algorithm for linear operator learning with shallow neural network An enhanced v-cycle mgnet model for operator learning in numerical partial di fferential equations

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.691278Z

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Observation 7386a1c4-b03e-4f3a-8780-496f673d1c37 · outbound

This paper cites Fv-mgnet: Fully connected v-cycle mgnet for interpretable time series forecasting.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Fv-mgnet: Fully connected v-cycle mgnet for interpretable time series forecasting

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.677273Z

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Observation 712ea0a1-670e-4f7f-8d29-8528e6bdbff6 · outbound

This paper cites Mod-net: A machine learning approach via model-operator-data network for solving pdes.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Mod-net: A machine learning approach via model-operator-data network for solving pdes

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.663240Z

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Observation 86e0a3f1-7f9e-4c37-9a11-7f6ba43ada39 · outbound

This paper cites Deepgreen: deep learning of green’s functions for nonlinear boundary value problems.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Deepgreen: deep learning of green’s functions for nonlinear boundary value problems

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.649702Z

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Observation 46fa9231-49fb-4728-af34-a0b0d69c1812 · outbound

This paper cites Bi-greennet: learning green’s functions by boundary integral network.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Bi-greennet: learning green’s functions by boundary integral network

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.636080Z

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Observation f0a5b751-3b95-4313-8660-e160815c9b4b · outbound

This paper cites Deep surrogate model for learning Green's function associated with linear reaction-diffusion operator.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Deep surrogate model for learning Green's function associated with linear reaction-diffusion operator

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:11:15.201190Z

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Observation c55a6070-5c02-4e50-8a59-0fb821b54c9f · outbound

This paper cites Deep Generalized Green's Functions.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Deep Generalized Green's Functions

Reference 55

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Observation 04d4dfb0-2f8b-4118-afbf-d029c638769d · outbound

This paper cites Learning green’s functions of linear reaction-di ffusion equations with application to 24 fast numerical solver.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Learning green’s functions of linear reaction-di ffusion equations with application to 24 fast numerical solver

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.621273Z

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Observation bd424eb6-8b88-403c-bdd2-782b06c9030d · outbound

This paper cites Green Multigrid Network.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Green Multigrid Network

Reference 57

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Orthogonal greedy algorithm for linear operator learning with shallow neural network Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:11:15.939246Z

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Observation 14db5389-32c4-4926-9203-6c6b46120644 · outbound

This paper cites Remarques sur un r ´esultat non publi´e de b.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Remarques sur un r ´esultat non publi´e de b

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.607921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 183a5337-1d00-48e4-99c8-e091b9d527a5 · outbound

This paper cites an unresolved cited work.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:11:15.594757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6ab9ae83-9d4c-4d47-a5a7-f2052e6b424d · outbound

This paper cites Multilevel multi-integration algorithm for acoustics.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Multilevel multi-integration algorithm for acoustics

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.581708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8a0d97b9-6263-45f6-99ef-5c53983bae08 · outbound

This paper cites Mallat and Zhifeng Zhang.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Mallat and Zhifeng Zhang

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.568801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a7cb17d8-7741-4316-a1bb-cef31156e099 · outbound

This paper cites A simple lemma on greedy approximation in hilbert space and convergence rates for projection pursuit regression and neural network training.

Orthogonal greedy algorithm for linear operator learning with shallow neural network A simple lemma on greedy approximation in hilbert space and convergence rates for projection pursuit regression and neural network training

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.555171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 20ee54ba-30f1-4036-a506-a7e5eba2170c · outbound

This paper cites an unresolved cited work.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-10T22:11:15.541477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 33e9e7fd-110f-4326-8180-2f44ae739dd4 · outbound

This paper cites Siegel and Jinchao Xu.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Siegel and Jinchao Xu

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.527457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 16233800-fa92-40a0-96e5-252225335041 · outbound

This paper cites Entropy-based convergence rates of greedy algorithms.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Entropy-based convergence rates of greedy algorithms

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.512760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ec834b22-4429-442f-b4d3-af0d7356da1d · outbound

This paper cites Universal approximation bounds for superpositions of a sigmoidal function.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Universal approximation bounds for superpositions of a sigmoidal function

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-10T22:11:14.915910Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd925179-69b9-468a-97c9-026281fe30c2 · outbound

This paper cites Hinging hyperplanes for regression, classification, and function approximation.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Hinging hyperplanes for regression, classification, and function approximation

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.499122Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e3d78d4f-d0e8-45c7-9853-91bf3366e2aa · outbound

This paper cites Approximation by combinations of relu and squared relu ridge functions with \ellˆ 1 and\ellˆ 0 controls.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Approximation by combinations of relu and squared relu ridge functions with \ellˆ 1 and\ellˆ 0 controls

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.484124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e0d43903-a3f7-4406-9945-96ee5451ec27 · outbound

This paper cites Tighter Sparse Approximation Bounds for ReLU Neural Networks.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Tighter Sparse Approximation Bounds for ReLU Neural Networks

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:11:15.154097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ab5291bf-9b73-4111-9db0-ac8c59d3d346 · outbound

This paper cites On the Activation Function Dependence of the Spectral Bias of Neural Networks.

Orthogonal greedy algorithm for linear operator learning with shallow neural network On the Activation Function Dependence of the Spectral Bias of Neural Networks

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-10T22:11:14.933227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f187499c-6889-4a50-af92-0084ecb3e9ff · outbound

This paper cites Bridging traditional and machine learning-based algorithms for solving pdes: the random feature method.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Bridging traditional and machine learning-based algorithms for solving pdes: the random feature method

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:16.112357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dc5463cb-468c-44c6-8217-fc6abfcdbe8e · outbound

This paper cites Can physics-informed neural networks beat the finite element method? IMA Journal of Applied Mathematics, page hxae011, 2024.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Can physics-informed neural networks beat the finite element method? IMA Journal of Applied Mathematics, page hxae011, 2024

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.470195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2f01ed4b-7f35-44fc-ad83-982570df4476 · outbound

This paper cites Why Shallow Networks Struggle to Approximate and Learn High Frequencies.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Why Shallow Networks Struggle to Approximate and Learn High Frequencies

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T22:11:14.945747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45c25b8c-d52f-4bbe-a651-9ce0a8542523 · outbound

This paper cites Monte Carlo methods in statistical physics, volume 7.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Monte Carlo methods in statistical physics, volume 7

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.456492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e5984706-077e-4b40-a154-e66dfc540e23 · outbound

This paper cites Gaussian processes for machine learning , volume 2.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Gaussian processes for machine learning , volume 2

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-10T22:11:14.954690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d14eb0ff-488d-4bd6-920b-e5da5868d760 · outbound

This paper cites A Driscoll, N.

Orthogonal greedy algorithm for linear operator learning with shallow neural network A Driscoll, N

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.433685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d324c4e9-7901-40f7-83c0-bd53c1e2d2fe · outbound

This paper cites Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Gmsh: A 3-d finite element mesh generator with built-in pre-and post-processing facilities

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.421115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2aadd2c8-9e86-471e-9453-bcf67289daa4 · outbound

This paper cites Baratta, Joseph P.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Baratta, Joseph P

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-10T22:11:14.966927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a70c6546-974f-4e31-91ed-91b49398c280 · outbound

This paper cites Gaussianrandomfields.jl: A julia package to generate and sample from gaussian random fields.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Gaussianrandomfields.jl: A julia package to generate and sample from gaussian random fields

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.400084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0e10c682-472f-4d83-ab7a-a23c26a8ac1b · outbound

This paper cites DeepXDE: A deep learning library for solving di fferential equations.

Orthogonal greedy algorithm for linear operator learning with shallow neural network DeepXDE: A deep learning library for solving di fferential equations

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:11:15.386808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1d0b609f-428f-47b9-8e40-dc1cff463d40 · outbound

This paper cites Rational neural networks.

Orthogonal greedy algorithm for linear operator learning with shallow neural network Rational neural networks

Reference 82

Resolution: verified exact; raw_fallback, observed 2026-08-10T22:11:15.101953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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