Paper Citation Record · LEDGER

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems

As of 11 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 1 inbound Pith citation observation for arXiv:2501.04608.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.04608 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:33:03.311862Z

measured 61 of 61 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:18:41.363598Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-09T14:18:41.545503Z

Reference resolution

60 of 60 outbound references displayed

verified exact12
verified fuzzy14
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External citation measurements

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Outbound references

Observation a5b89c02-64a3-4f3d-9f92-5d9b1bd0ad87 · outbound

This paper cites an unresolved cited work.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Unresolved cited work

Reference 1

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Observation bfb986be-85a3-4b35-ad16-eb5969420a5b · outbound

This paper cites an unresolved cited work.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Unresolved cited work

Reference 2

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Observation 0a8f0ef9-444f-4405-86b5-6d4a4cb02714 · outbound

This paper cites Candes and M.B.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Candes and M.B

Reference 3

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Observation 9b5efa6e-a6ef-4d5e-a5d5-c0d9353ed761 · outbound

This paper cites Compressive sensing [lecture notes].

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Compressive sensing [lecture notes]

Reference 4

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Observation 5b779e67-179f-4482-bcb2-2d316ec0ab19 · outbound

This paper cites Donoho, Arian Maleki, and Andrea Montanari.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Donoho, Arian Maleki, and Andrea Montanari

Reference 5

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Observation d2abadd4-6e67-40aa-aefa-63c8f9c23d47 · outbound

This paper cites Metzler, Arian Maleki, and Richard G.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Metzler, Arian Maleki, and Richard G

Reference 6

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Observation d2a200fb-b84b-4c60-932c-4a910261827b · outbound

This paper cites Venkatakrishnan, Charles A.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Venkatakrishnan, Charles A

Reference 7

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Observation 043414cb-d68b-4f71-b094-2cb395f4682f · outbound

This paper cites From compression to compressed sensing.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems From compression to compressed sensing

Reference 8

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Observation df5d6c29-47eb-4422-867f-a642507117b7 · outbound

This paper cites An efficient algorithm for compression-based compressed sensing.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems An efficient algorithm for compression-based compressed sensing

Reference 9

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Observation f9a6b056-0eb9-4b6e-b50d-e552771d1b85 · outbound

This paper cites The Little Engine that Could: Regularization by Denoising (RED).

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems The Little Engine that Could: Regularization by Denoising (RED)

Reference 10

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Observation b5a87355-a52d-483f-b4d6-abae00165d29 · outbound

This paper cites Deep ADMM-Net for Compres- sive Sensing MRI.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Deep ADMM-Net for Compres- sive Sensing MRI

Reference 11

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Observation 8e7d81e7-2298-4bb4-a42d-a3f61bcd4627 · outbound

This paper cites Baraniuk.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Baraniuk

Reference 12

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Source-reported events for the cited work

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Observation 19a74abb-f667-4627-85fb-18d2f9bef828 · outbound

This paper cites Rick Chang, Chun-Liang Li, Barnabas Poczos, B.V.K.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Rick Chang, Chun-Liang Li, Barnabas Poczos, B.V.K

Reference 13

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Observation e8e56e21-9160-4b03-a753-0b9ec73a51ed · outbound

This paper cites DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks

Reference 14

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Observation 2db6fbef-a26b-48bb-b8cf-88559c0eba0b · outbound

This paper cites Learned D-AMP: Principled Neural Network based Compressive Image Recovery.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Learned D-AMP: Principled Neural Network based Compressive Image Recovery

Reference 15

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Observation 8b0ec7a1-8a5f-4eb8-826f-f13c855538f0 · outbound

This paper cites McCann, Kyong Hwan Jin, and Michael Unser.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems McCann, Kyong Hwan Jin, and Michael Unser

Reference 16

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Observation c9770eeb-5bae-4276-85f0-9d86214e6ee9 · outbound

This paper cites ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

Reference 17

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Observation c885887c-b0b9-419d-a047-112034d66bd9 · outbound

This paper cites Unrolled Optimization with Deep Priors.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Unrolled Optimization with Deep Priors

Reference 18

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Observation 65356118-9e16-4c35-a984-3d2dc8313688 · outbound

This paper cites Hajnal, Anthony N.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Hajnal, Anthony N

Reference 19

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Observation dbdddf0e-f1fe-4032-aa40-34ad2556d99b · outbound

This paper cites Neumann Networks for Inverse Problems in Imaging.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Neumann Networks for Inverse Problems in Imaging

Reference 20

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Observation 61e96dc8-d472-485b-a6dc-cfe5e8dc4aeb · outbound

This paper cites Aggarwal, Merry P.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Aggarwal, Merry P

Reference 21

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Observation cf0dc7b5-6f86-4248-b3fc-b5f9c08a3d25 · outbound

This paper cites Metzler, Richard G.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Metzler, Richard G

Reference 22

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Observation 67daee49-6110-4128-8598-575cb5c758cf · outbound

This paper cites Compressed Sensing with Deep Image Prior and Learned Regularization.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Compressed Sensing with Deep Image Prior and Learned Regularization

Reference 23

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Observation 9a87fec9-4a98-4c3a-bde2-3ba6865b4a6c · outbound

This paper cites Deep Equilibrium Architectures for Inverse Problems in Imaging.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Deep Equilibrium Architectures for Inverse Problems in Imaging

Reference 24

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Observation a84a7b10-0738-43f5-9049-9287fa742347 · outbound

This paper cites Model Adaptation for Inverse Problems in Imaging.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Model Adaptation for Inverse Problems in Imaging

Reference 25

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Observation 3b32f6b0-445c-4794-b415-1a630b3e1562 · outbound

This paper cites Stochastic solutions for linear inverse problems using the prior implicit in a denoiser.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Stochastic solutions for linear inverse problems using the prior implicit in a denoiser

Reference 26

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Observation 088314e8-d8b1-445b-aa76-215d6fa91d72 · outbound

This paper cites Shastri, Rizwan Ahmad, Christopher A.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Shastri, Rizwan Ahmad, Christopher A

Reference 27

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This paper cites Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion

Reference 28

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Observation f025cad1-bf2b-4636-be6d-ee7489beab5e · outbound

This paper cites Physics-Inspired Compressive Sensing: Beyond deep unrolling.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Physics-Inspired Compressive Sensing: Beyond deep unrolling

Reference 29

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Observation 3a040b29-d26b-4890-86a7-efd1612c18b9 · outbound

This paper cites Plug-and- play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Plug-and- play methods for integrating physical and learned models in computational imaging: Theory, algorithms, and applications

Reference 30

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Observation 04aaf38c-cff2-4b43-9f6c-c78bac9b6630 · outbound

This paper cites Block coordinate plug-and-play methods for blind inverse problems.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Block coordinate plug-and-play methods for blind inverse problems

Reference 31

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Observation 84818cae-3bea-466e-8f4e-a3501fd217c8 · outbound

This paper cites Ptychodv: Vision transformer-based deep unrolling network for ptychographic image reconstruction.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Ptychodv: Vision transformer-based deep unrolling network for ptychographic image reconstruction

Reference 32

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Observation a95a90cd-400e-4217-a9fb-2deedfeb18c5 · outbound

This paper cites Stochastic Deep Restoration Priors for Imaging Inverse Problems.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Stochastic Deep Restoration Priors for Imaging Inverse Problems

Reference 33

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This paper cites Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems

Reference 34

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Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Practical Compact Deep Compressed Sensing

Reference 35

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Observation a561c2ed-a7e2-4ae9-a2ad-8dac832e1639 · outbound

This paper cites Self-Supervised Scalable Deep Compressed Sensing.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Self-Supervised Scalable Deep Compressed Sensing

Reference 36

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Observation ace5ac1e-01f3-41dc-82e7-47a534f083dd · outbound

This paper cites MRI recovery with self- calibrated denoisers without fully-sampled data.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems MRI recovery with self- calibrated denoisers without fully-sampled data

Reference 37

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Observation 1fc8f081-3d8e-41bc-97a0-4f542f2fdfad · outbound

This paper cites Kamilov, and Brendt Wohlberg.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Kamilov, and Brendt Wohlberg

Reference 38

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Observation c5cda460-5f45-412c-b4c9-4b5de7c3f9f0 · outbound

This paper cites ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements

Reference 39

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Observation 5d6091bf-2de0-41d0-8312-c0a6ab32f6c4 · outbound

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Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Learning fast approximations of sparse coding

Reference 40

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Observation 7f4fd99b-1e12-4eee-aa1e-ece08c7f409a · outbound

This paper cites Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing

Reference 41

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Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Compression-based compressed sensing

Reference 42

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Observation 1a45530b-ef48-47c3-9260-ee4e1087028e · outbound

This paper cites Neural Proximal Gradient Descent for Compressive Imaging, June.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Neural Proximal Gradient Descent for Compressive Imaging, June

Reference 43

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Observation 5694c0e0-d372-4bb8-a405-9f5564d66fa1 · outbound

This paper cites Deep Algorithm Unrolling for Biomedical Imaging.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Deep Algorithm Unrolling for Biomedical Imaging

Reference 44

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Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems A Fast Iterative Shrinkage-Thresholding Algorithm for Linear In- verse Problems

Reference 45

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This paper cites GSISTA-Net: generalized structure ISTA networks for image compressed sensing based on optimized unrolling algorithm.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems GSISTA-Net: generalized structure ISTA networks for image compressed sensing based on optimized unrolling algorithm

Reference 46

Resolution: verified exact; doi, observed 2026-08-10T21:33:03.362897Z

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Observation 21fd0c2c-a254-4cbd-9c43-cba65c9e196b · outbound

This paper cites AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing

Reference 47

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Observation 3ba11e21-1a49-4f18-b41c-6f7d51de2ee3 · outbound

This paper cites Convolutional Neural Networks With Intermediate Loss for 3D Super-Resolution of CT and MRI Scans.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Convolutional Neural Networks With Intermediate Loss for 3D Super-Resolution of CT and MRI Scans

Reference 48

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Observation 32dff860-6270-48b3-a60c-98c12b0c28d1 · outbound

This paper cites Deep Generalized Unfolding Networks for Image Restoration.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Deep Generalized Unfolding Networks for Image Restoration

Reference 49

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Observation 1d43c39a-7099-41a0-a76c-e01bddc526ed · outbound

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Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Rethinking the inception architecture for computer vision

Reference 50

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Observation dcb2ebea-ae1b-46c9-a527-12c8f681bee6 · outbound

This paper cites Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

Reference 51

Resolution: verified exact; raw_fallback, observed 2026-08-10T21:33:03.565905Z

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Observation dcf2ebaa-1609-4455-8278-c6ddb1c49e61 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Deep Residual Learning for Image Recognition

Reference 52

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Observation 32fb0808-6062-46fd-97ae-d81c2a6f14ee · outbound

This paper cites Identity Mappings in Deep Residual Networks.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Identity Mappings in Deep Residual Networks

Reference 53

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Observation 9e3f3d79-9cd5-41f2-947f-b6c933495a57 · outbound

This paper cites Convolu- tional neural networks: an overview and application in radiology.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Convolu- tional neural networks: an overview and application in radiology

Reference 54

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Observation 3dcc5c14-bf12-48b1-8aba-db1e9a38e5f0 · outbound

This paper cites Sub- sequently, we normalize A according to A′ = A/∥A∥∞,2 to obtain our sampling matrix.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Sub- sequently, we normalize A according to A′ = A/∥A∥∞,2 to obtain our sampling matrix

Reference 58

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Observation fdb679d5-55b5-402b-8fe8-3f10c7800807 · outbound

This paper cites an unresolved cited work.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:33:05.462126Z

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

Observation 6a3ed32e-eb13-4589-8b99-48a05aec24ac · outbound

This paper cites an unresolved cited work.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-10T21:33:05.446070Z

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

Observation 20210803-a0c3-4c23-bae2-273a374fcba6 · outbound

This paper cites doi: 10.1002/mrm.21391.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems doi: 10.1002/mrm.21391

Reference 2007

Resolution: unresolved; no resolver link, observed 2026-08-10T21:33:03.037144Z

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Observation ec245428-8eeb-4744-b150-84a36701123f · outbound

This paper cites doi: 10.1073/pnas.0909892106.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems doi: 10.1073/pnas.0909892106

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-10T21:33:03.056175Z

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Observation 8e9f6309-3751-4a4f-b8b0-7c65298f89d2 · outbound

This paper cites Neural Proximal Gradient Descent for Compressive Imaging.

Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems Neural Proximal Gradient Descent for Compressive Imaging

Reference 2018

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Pith citing papers

Observation bab3a3ca-2f6e-43cb-ac2f-babb867163d6 · inbound

How to warm-start your unfolding network cites this paper.

How to warm-start your unfolding network Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-09T14:18:41.550065Z

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