Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery | Paper Citation Record

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This paper cites Blind deconvolution using con- vex programming,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Blind deconvolution using con- vex programming,

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This paper cites Gespar: Efficient phase retrieval of sparse signals,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Gespar: Efficient phase retrieval of sparse signals,

Reference 2

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This paper cites Neural blind deconvolution using deep priors,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Neural blind deconvolution using deep priors,

Reference 3

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This paper cites A neural network approach for the blind deconvolution of turbulent flows,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery A neural network approach for the blind deconvolution of turbulent flows,

Reference 4

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This paper cites Feature-based phase retrieval wavefront sensing approach using machine learning,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Feature-based phase retrieval wavefront sensing approach using machine learning,

Reference 5

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This paper cites Blind image deconvolution using deep generative priors,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Blind image deconvolution using deep generative priors,

Reference 6

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This paper cites Relaxation of align- ment errors and phase calibration in computational frequency-diverse imaging using phase retrieval,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Relaxation of align- ment errors and phase calibration in computational frequency-diverse imaging using phase retrieval,

Reference 7

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This paper cites Blind deconvolution using a normalized sparsity measure,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Blind deconvolution using a normalized sparsity measure,

Reference 8

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This paper cites Understanding blind deconvolution algorithms,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Understanding blind deconvolution algorithms,

Reference 9

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This paper cites Normalized blind deconvolution,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Normalized blind deconvolution,

Reference 10

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This paper cites Understanding and evaluating blind deconvolution algorithms,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Understanding and evaluating blind deconvolution algorithms,

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This paper cites Phase retrieval with application to optical imaging: A contemporary overview,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Phase retrieval with application to optical imaging: A contemporary overview,

Reference 12

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This paper cites The numerics of phase retrieval,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery The numerics of phase retrieval,

Reference 13

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This paper cites The phase retrieval problem,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery The phase retrieval problem,

Reference 14

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This paper cites Phase retrieval via Wirtinger flow: Theory and algorithms,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Phase retrieval via Wirtinger flow: Theory and algorithms,

Reference 15

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This paper cites Class-Specific Blind Deconvolutional Phase Retrieval Under a Generative Prior.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Class-Specific Blind Deconvolutional Phase Retrieval Under a Generative Prior

Reference 16

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This paper cites Simultaneous phase retrieval and blind deconvolution via convex programming,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Simultaneous phase retrieval and blind deconvolution via convex programming,

Reference 17

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This paper cites Blind deconvolution meets phase retrieval in optical wireless communications,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Blind deconvolution meets phase retrieval in optical wireless communications,

Reference 18

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This paper cites Simultaneous blind deconvolution and phase retrieval with tensor iterative hard thresholding,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Simultaneous blind deconvolution and phase retrieval with tensor iterative hard thresholding,

Reference 19

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This paper cites Structured local optima in sparse blind deconvolution,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Structured local optima in sparse blind deconvolution,

Reference 20

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This paper cites Global geometry of multichannel sparse blind de- convolution on the sphere,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Global geometry of multichannel sparse blind de- convolution on the sphere,

Reference 21

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This paper cites The nonsmooth landscape of blind deconvolution.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery The nonsmooth landscape of blind deconvolution

Reference 22

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This paper cites Solving random quadratic systems of equations is nearly as easy as solving linear systems,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Solving random quadratic systems of equations is nearly as easy as solving linear systems,

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This paper cites A geometric analysis of phase retrieval,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery A geometric analysis of phase retrieval,

Reference 24

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This paper cites On the optimization landscape of tensor decompo- sitions,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery On the optimization landscape of tensor decompo- sitions,

Reference 25

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This paper cites Optimization landscape of tucker decomposi- tion,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Optimization landscape of tucker decomposi- tion,

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Local and global convergence of general burer- monteiro tensor optimizations,

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Landscape analysis of an improved power method for tensor decomposition,

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Guaranteed nonconvex factorization approach for tensor train recovery,

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This paper cites A Scalable Factorization Approach for High-Order Structured Tensor Recovery.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery A Scalable Factorization Approach for High-Order Structured Tensor Recovery

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Tensor decomposition for signal processing and machine learning,

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Tensorizing neural networks,

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Tensor fusion network for multimodal sentiment analysis,

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Towards quantum machine learning with tensor networks,

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Tensor decompositions and applications,

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This paper cites A new trunca- tion strategy for the higher-order singular value decomposition,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery A new trunca- tion strategy for the higher-order singular value decomposition,

Reference 36

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This paper cites Quantitative phase- amplitude microscopy i: optical microscopy,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Quantitative phase- amplitude microscopy i: optical microscopy,

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Phase retrieval and differential phase-contrast imaging with low-brilliance X-ray sources,

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This paper cites Contrast in the electron spectroscopic imaging mode of a tem: Iv. thick specimens imaged by the most-probable energy loss,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Contrast in the electron spectroscopic imaging mode of a tem: Iv. thick specimens imaged by the most-probable energy loss,

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Born and E

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery The landscape of non-convex empirical risk with degenerate population risk,

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Landscape correspondence of empirical and population risks in the eigendecomposition problem,

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Compressed sensing,

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This paper cites Robust uncertainty principles: Ex- act signal reconstruction from highly incomplete frequency information,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Robust uncertainty principles: Ex- act signal reconstruction from highly incomplete frequency information,

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Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery An introduction to compressive sampling,

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This paper cites Iterative hard thresholding for low CP-rank tensor models,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Iterative hard thresholding for low CP-rank tensor models,

Reference 47

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This paper cites Low rank tensor recovery via iterative hard thresholding,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Low rank tensor recovery via iterative hard thresholding,

Reference 48

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This paper cites Quantum state tomography for matrix product density operators,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Quantum state tomography for matrix product density operators,

Reference 49

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This paper cites Nonconvex low-rank symmetric tensor completion from noisy data,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Nonconvex low-rank symmetric tensor completion from noisy data,

Reference 50

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This paper cites Provable tensor-train format tensor completion by Riemannian optimization,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Provable tensor-train format tensor completion by Riemannian optimization,

Reference 51

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This paper cites Weakly convex optimization over Stiefel manifold using Riemannian subgradient-type methods,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Weakly convex optimization over Stiefel manifold using Riemannian subgradient-type methods,

Reference 52

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Observation 332c4d23-c208-4e3b-bb86-83932a3ecb89 · outbound

This paper cites Tensor-train decomposition,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Tensor-train decomposition,

Reference 53

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Observation f3da5cd4-d947-44de-ad3a-837cc9876ea4 · outbound

This paper cites An optimal statistical and computational framework for generalized tensor estimation,.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery An optimal statistical and computational framework for generalized tensor estimation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:49.436734Z

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Observation 8fdbcc5f-8206-48d6-bd08-6451be8cf7a2 · outbound

This paper cites Proof of(24)by induction:First note that (24) holds att= 0by initialization.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery Proof of(24)by induction:First note that (24) holds att= 0by initialization

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:49.419517Z

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Observation 4584495e-2f8c-40d9-ac88-a3f54930a0d3 · outbound

This paper cites APPENDIXF PROOF OFTHEOREMV.4 Proof.We begin by establishing a fundamental probabilistic property for the noise term.

Landscape Analysis of Simultaneous Blind Deconvolution and Phase Retrieval via Structured Low-Rank Tensor Recovery APPENDIXF PROOF OFTHEOREMV.4 Proof.We begin by establishing a fundamental probabilistic property for the noise term

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:49.405745Z

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