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Source: paper_references, paper_reference_links, observed 2026-08-01T18:09:44.868634Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 0 inbound Pith citation observations for arXiv:2607.17390.
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Source: paper_references, paper_reference_links, observed 2026-08-01T18:09:44.868634Z
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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
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71 of 71 outbound references displayed
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Tensordecompositionforsignalprocessing andmachinelearning,
Reference 1
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Tensorcompletionalgorithmsinbigdataanalytics,
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Tensor-traindecomposition,
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Tensor-train decomposition for image classification problems,
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Kernel Regression with Tensor Trains and Hadamard Overparameterization The Tucker and Tensor Train decompositions,
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Kernel Regression with Tensor Trains and Hadamard Overparameterization On manifolds of tensors of fixed TT-rank,
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Berlin: Springer,2022
Reference 8
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Theoryofreproducingkernels,
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Cambridge,MA:MITPress,2002
Reference 10
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Some results on Tchebycheffian spline functions,
Reference 11
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Nonparametric low-rank tensor imputation,
Reference 12
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Tensor Decomposition Meets RKHS: Efficient Algorithms for Smooth and Misaligned Data
Reference 13
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Observation 86b6ba57-dde7-41ef-91d7-fe4383a7a736 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization When bayesian tensor completion meets multioutput gaussian processes: Functional universality and rank learning,
Reference 14
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Kernel-based tensor partial least squares for reconstruction of limb move- ments,
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Gradient-based optimization for regression in the functional tensor-train format,
Reference 16
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Observation 7bce82ab-6959-421e-88f5-6474e25ab5fe · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Kernel Bayesian tensor ring decomposition for multiway data recovery,
Reference 17
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Observation 24b7f9a3-310e-48e8-ada7-ea90c99940a6 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Györfi, M
Reference 18
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Observation 92e4954e-27ca-4556-8707-f20ae5e1e908 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Multilinear kernel regression and imputation via manifold learning,
Reference 19
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Observation 7a99f9a4-5c70-45f8-a4a1-d02eed501e9b · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Imputation of time-varying edge flows in graphs by multilinear kernel regression and manifoldlearning,
Reference 20
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Estimating dynamic graph flows with kernel models and Hadamard-structured Riemannian constraints,
Reference 21
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Observation 42857074-be32-4c4c-a4b3-4c246c9a06f7 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Riemannianoptimizationforhigh-dimensionaltensorcompletion,
Reference 22
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Observation 5efa42ed-c888-4cd4-89ee-4e6dbaa87b84 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Taking the 4D nature of fMRI data into account promises significant gains in data completion,
Reference 23
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Kernel Regression with Tensor Trains and Hadamard Overparameterization LASSO, fractional norm and structured sparse estimation using a Hadamard product parametrization,
Reference 24
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Kernel Regression with Tensor Trains and Hadamard Overparameterization The tail-Hadamard product parametrization algorithm for compressed sensing,
Reference 25
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Reference 26
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Observation d414f07e-e975-449f-93f2-850c49f51690 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization
Reference 27
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Kernel regression via tensor trains with Hadamard overparametrization and imputationofdynamicgraphedgeflows,
Reference 28
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Observation 868e36bd-04cc-43f4-96c0-925f2cf4c4b7 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization LearninggeneralGaussiankernelhyperparametersofSVMsusingoptimization onsymmetricpositive-definitematricesmanifold,
Reference 29
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Tensordecompositionsandapplications,
Reference 30
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Observation 50d2dd34-7c29-4ec5-851b-8b9133712dcb · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Tensor networks for dimensionality reduction and large- scale optimization—Part 1: Low-rank tensor decompositions,
Reference 31
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Observation 34f168e7-eaae-4633-87ba-7c707a6585d5 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization PrincetonUniversityPress,2008
Reference 32
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Observation 15696782-8009-47f9-8651-fe7b4ce3b058 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Geometric methods on low-rank matrix and tensor manifolds,
Reference 33
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Observation ce226d29-0ec3-4d06-88b9-77878edfa60e · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Nonlinear dimensionality reduction by locally linear embedding,
Reference 34
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Observation 5943a56c-8e7e-4213-83b6-d3c533ee6f11 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Sparsemanifoldclusteringandembedding,
Reference 35
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Using the Nyström method to speed up kernel machines,
Reference 36
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Observation 2d28d50c-7fcb-4d5d-88c4-54645e352782 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Sparse multidimensional scaling using landmark points,
Reference 37
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Observation 61e4018c-0fde-46bd-90a6-8d434d95b51d · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Unconstrainedparametrizationsforvariance-covariancematrices,
Reference 38
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Observation b72e2bc0-6d37-4b33-a196-23160f6bbdb2 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Springer,2006
Reference 39
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Observation 1e1ddd2d-adad-4f69-bfbc-5033fe6ed655 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Low-rank matrix completion by Riemannian optimization,
Reference 40
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Observation a1c3e20f-0786-4150-9655-ec1beee9b634 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Tensorringdecomposition: Optimizationlandscapeandone-loopconvergenceofalternatingleastsquares,
Reference 41
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Observation 23265c35-73e8-48e6-9ea1-d5d7bbfe0d50 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Riemannian preconditioned algorithms for tensor completion via tensor ring decomposition,
Reference 42
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Observation ce33fddd-3dc5-4132-8799-1c0736b2a4ae · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Impact oftheresolutionofbrainparcelsonconnectome-wideassociationstudiesinfMRI,
Reference 43
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Observation e3ca6b03-41a5-4bd4-bdcf-31b8163df3ff · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization BOLD5000, a public fMRI dataset while viewing 5000 visualimages,
Reference 44
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Observation 8db2f87b-a667-4dfa-8e80-232f13a3ad2e · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization fMRIPrep: arobustpreprocessingpipelineforfunctionalMRI,
Reference 45
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Observation 10994cd2-26f9-4b39-be72-93d7ea0dbdc3 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Data adaptive RKHS Tikhonov regularization for learning kernels in operators,
Reference 46
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Observation 6fab9d5c-e1fb-468b-9356-a54d376ead6d · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization CambridgeUniversityPress,2010
Reference 47
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Kernel Regression with Tensor Trains and Hadamard Overparameterization HodgeLaplaciansongraphs,
Reference 48
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Observation b91f4e81-dd9c-4733-9d41-5b1d10d017c8 · outbound
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Reference 49
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Observation 71b6c1e2-6bea-436a-848b-096c5599c86c · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Topological signal processing over simplicial complexes,
Reference 50
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Observation 905d29c5-f3fc-43a3-a86c-56f5ffb27e27 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Networksbeyondpairwiseinteractions: Structureanddynamics,
Reference 51
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Reference 52
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Reference 53
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Reference 54
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Reference 55
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Observation 62407065-c0c2-4ae8-978e-5e61a28862c8 · outbound
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Reference 56
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Observation 7dcbefae-0081-4bd6-8cd6-77462637a378 · outbound
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Reference 57
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Reference 58
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Position: Topologicaldeeplearningisthenewfrontierforrelationallearning,
Reference 59
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Observation a9ce7ba1-a3d0-4349-ba3f-36e4c72434e2 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Transportation networks for research,
Reference 60
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Kernel Regression with Tensor Trains and Hadamard Overparameterization UXsim: LightweightmesoscopictrafficflowsimulatorinpurePython,
Reference 61
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Signal processing on product spaces,
Reference 62
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Kernel Regression with Tensor Trains and Hadamard Overparameterization HodgeNet: Graphneuralnetworksforedgedata,
Reference 63
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Robusttensorringdecompositionforurbantrafficdataimputation,
Reference 64
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Observation 7f6d8d62-2344-4f2d-bbf0-e8ad7cd473be · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Tensordenoisingusinglow-ranktensortraindecomposition,
Reference 65
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Kernel Regression with Tensor Trains and Hadamard Overparameterization MosttensorproblemsareNP-hard,
Reference 66
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Observation 9f52fc58-7fe1-473b-bd97-55447f8b841d · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Projection-likeretractionsonmatrixmanifolds,
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Reference 68
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Kernel Regression with Tensor Trains and Hadamard Overparameterization quasi-optimal
Reference 69
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Kernel Regression with Tensor Trains and Hadamard Overparameterization Cν isderivedbythechainruleofdifferentiationasfollows ∂L ∂X (i1,
Reference 70
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Observation 604a7e71-a59e-4b6d-829c-ea5cf99c5fd2 · outbound
Kernel Regression with Tensor Trains and Hadamard Overparameterization Therefore,theEuclideangradientw.r.t
Reference 71
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