Paper Citation Record · LEDGER

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices

As of 17 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2608.12982.

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

pith.paper-citation-record.v1
2608.12982 v1

Coverage vector

measured 36 of 36 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

36 of 36 outbound references displayed

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External citation measurements

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Outbound references

Observation ee99dba3-bedf-44b4-a4cf-b339f8f309a0 · outbound

This paper cites Compressed Learning: A Deep Neural Network Approach.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Compressed Learning: A Deep Neural Network Approach

Reference 1

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This paper cites Contour detec- tion and hierarchical image segmentation.IEEE transactions on pattern analysis and machine intelligence, 33(5):898–916, 2010.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Contour detec- tion and hierarchical image segmentation.IEEE transactions on pattern analysis and machine intelligence, 33(5):898–916, 2010

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.800101Z

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Observation 666eb96f-b2fc-4888-94ea-232811dd975c · outbound

This paper cites A simple proof of the restricted isometry property for random matrices.Constructive approxi- mation, 28:253–263, 2008.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices A simple proof of the restricted isometry property for random matrices.Constructive approxi- mation, 28:253–263, 2008

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.785359Z

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Observation 8bcdcaee-2d6e-4ac2-ad76-922b3943fc34 · outbound

This paper cites A fast iterative shrinkage-thresholding algorithm for linear inverse problems.SIAM journal on imaging sciences, 2(1):183–202, 2009.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices A fast iterative shrinkage-thresholding algorithm for linear inverse problems.SIAM journal on imaging sciences, 2(1):183–202, 2009

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T19:19:52.073882Z

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Observation 0f20ac03-29ca-42ee-af56-3369c20b48da · outbound

This paper cites Compressed sensing using generative models.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Compressed sensing using generative models

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.758699Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c6b06f87-8efe-4505-953f-c988509fd860 · outbound

This paper cites Explicit constructions of rip matrices and related problems.Duke Mathematical Jour- nal, 2011.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Explicit constructions of rip matrices and related problems.Duke Mathematical Jour- nal, 2011

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.740476Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 17fcf446-3852-49fb-9b7c-213f6fde4f3c · outbound

This paper cites Sparse signal and image recovery from compressive samples.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Sparse signal and image recovery from compressive samples

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.725209Z

Source-reported events for the cited work

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Observation 3858fc51-7305-4b2f-aabc-deedef4161a4 · outbound

This paper cites Sparsity and incoherence in compressive sam- pling.Inverse problems, 23(3):969–985, 2007.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Sparsity and incoherence in compressive sam- pling.Inverse problems, 23(3):969–985, 2007

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.709598Z

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Observation e2d07f38-86c9-4242-abb3-dfff9cc1b23e · outbound

This paper cites The restricted isometry property and its implications for com- pressed sensing.Comptes rendus mathematique, 346(9-10):589–592, 2008.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices The restricted isometry property and its implications for com- pressed sensing.Comptes rendus mathematique, 346(9-10):589–592, 2008

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.692959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 996a2234-0779-4326-81b9-74f26a86ae45 · outbound

This paper cites Robust uncertainty princi- ples: Exact signal reconstruction from highly incomplete frequency information.IEEE Transactions on information theory, 52(2):489–509, 2006.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Robust uncertainty princi- ples: Exact signal reconstruction from highly incomplete frequency information.IEEE Transactions on information theory, 52(2):489–509, 2006

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.677781Z

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Observation 9ff495ee-3c56-41bd-adc5-84d5509dc703 · outbound

This paper cites an unresolved cited work.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Unresolved cited work

Reference 11

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Observation 9623fcd6-d0b9-412c-9731-0c5309ddedee · outbound

This paper cites Decoding by linear programming.IEEE trans- actions on information theory, 51(12):4203–4215, 2005.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Decoding by linear programming.IEEE trans- actions on information theory, 51(12):4203–4215, 2005

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.649320Z

Source-reported events for the cited work

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Observation 0d39b691-d3e0-4b07-b2b1-09a82bb3e8fa · outbound

This paper cites Near-optimal signal recovery from random projections: Universal encoding strategies?IEEE transactions on information theory, 52(12):5406–5425, 2006.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Near-optimal signal recovery from random projections: Universal encoding strategies?IEEE transactions on information theory, 52(12):5406–5425, 2006

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.633367Z

Source-reported events for the cited work

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Observation 08c81234-e519-4d18-b707-e44890234ff6 · outbound

This paper cites Compressed sensing and best k-term approximation.Journal of the American mathematical society, 22(1):211–231, 2009.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Compressed sensing and best k-term approximation.Journal of the American mathematical society, 22(1):211–231, 2009

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.617226Z

Source-reported events for the cited work

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Observation 3a0fe7ae-f8f5-41e3-9f46-079ba253521d · outbound

This paper cites Introduction to compressed sensing., 2012.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Introduction to compressed sensing., 2012

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.601085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c10c49b9-bcf1-40a6-b1fd-9c28ccf852d4 · outbound

This paper cites Deterministic constructions of compressed sensing matrices.Journal of complexity, 23(4-6):918–925, 2007.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Deterministic constructions of compressed sensing matrices.Journal of complexity, 23(4-6):918–925, 2007

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.585613Z

Source-reported events for the cited work

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Observation df4982e5-9e1e-4157-a21f-657af75b4e90 · outbound

This paper cites Compressed sensing.IEEE Transactions on information theory, 52(4):1289–1306, 2006.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Compressed sensing.IEEE Transactions on information theory, 52(4):1289–1306, 2006

Reference 17

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Observation 6ba0d9b2-e4b9-4986-abf2-f80c837be6f9 · outbound

This paper cites Optimally sparse representation in general (nonorthogonal) dictionaries viaℓ 1 minimization.Proceedings of the National Academy of Sciences, 100(5):2197–2202, 2003.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Optimally sparse representation in general (nonorthogonal) dictionaries viaℓ 1 minimization.Proceedings of the National Academy of Sciences, 100(5):2197–2202, 2003

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.558213Z

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Observation c7461423-eaf9-4a3e-869f-03559d08292b · outbound

This paper cites Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization.IEEE Transac- tions on Image Processing, 18(7):1395–1408, 2009.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Learning to sense sparse signals: Simultaneous sensing matrix and sparsifying dictionary optimization.IEEE Transac- tions on Image Processing, 18(7):1395–1408, 2009

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.542012Z

Source-reported events for the cited work

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Observation 8aa1b829-0bcc-43e0-9e69-df3fedfd8bf5 · outbound

This paper cites Optimized projections for compressed sensing.IEEE Transactions on Signal Processing, 55(12):5695–5702, 2007.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Optimized projections for compressed sensing.IEEE Transactions on Signal Processing, 55(12):5695–5702, 2007

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.525506Z

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Observation 324308a5-8c8b-4b75-a6ef-4eb94e3629e1 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Imagenet classification with deep convolutional neural networks.Advances in neural information processing systems, 25, 2012

Reference 21

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Observation ec07ae64-696a-4735-accb-52bec267fe7b · outbound

This paper cites Gradient-based learn- ing applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 1998.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Gradient-based learn- ing applied to document recognition.Proceedings of the IEEE, 86(11):2278–2324, 1998

Reference 22

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Observation f7869ad8-f145-47aa-99f4-43d92d7f1c8a · outbound

This paper cites Deep learning face attributes in the wild.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Deep learning face attributes in the wild

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T19:19:52.169608Z

Source-reported events for the cited work

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Observation 992329cd-003b-4f2f-bdea-1b204f59aec4 · outbound

This paper cites Convolutional neural networks for noniterative reconstruction of compressively sensed images.IEEE Transactions on Computational Imaging, 4(3):326–340, 2018.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Convolutional neural networks for noniterative reconstruction of compressively sensed images.IEEE Transactions on Computational Imaging, 4(3):326–340, 2018

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.478315Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation aaaf8a41-462f-4354-82a3-ef79a106b096 · outbound

This paper cites Algorithm unrolling: Interpretable, ef- ficient deep learning for signal and image processing.IEEE Signal Processing Magazine, 38(2):18–44, 2021.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Algorithm unrolling: Interpretable, ef- ficient deep learning for signal and image processing.IEEE Signal Processing Magazine, 38(2):18–44, 2021

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.463819Z

Source-reported events for the cited work

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Observation e008e84a-3f85-494b-ac96-09a804dd5cc0 · outbound

This paper cites A deep learning approach to structured signal recovery.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices A deep learning approach to structured signal recovery

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.449077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 05fecace-2f31-45b8-98a8-60e4bf687493 · outbound

This paper cites Compressed sens- ing: A simple deterministic measurement matrix and a fast recovery algorithm.IEEE Transactions on Instrumentation and Measurement, 64(12):3405–3413, 2015.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Compressed sens- ing: A simple deterministic measurement matrix and a fast recovery algorithm.IEEE Transactions on Instrumentation and Measurement, 64(12):3405–3413, 2015

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.433685Z

Source-reported events for the cited work

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Observation b6606f96-3456-4eb7-9220-a63be78c9e43 · outbound

This paper cites Ima- genet large scale visual recognition challenge.International journal of computer vision, 115(3):211–252, 2015.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Ima- genet large scale visual recognition challenge.International journal of computer vision, 115(3):211–252, 2015

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.417195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 46ee65bf-91fc-4d62-ba33-f8d5a1291338 · outbound

This paper cites Image compressed sensing using convolutional neural network.IEEE Transactions on Image Processing, 29:375– 388, 2019.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Image compressed sensing using convolutional neural network.IEEE Transactions on Image Processing, 29:375– 388, 2019

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.401567Z

Source-reported events for the cited work

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Observation 54505081-e35d-4357-aacb-36ea315d1a56 · outbound

This paper cites On the existence of equiangular tight frames.Linear Algebra and its applications, 426(2-3):619– 635, 2007.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices On the existence of equiangular tight frames.Linear Algebra and its applications, 426(2-3):619– 635, 2007

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.385273Z

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Observation 86df691a-bbb5-48f7-97d3-6aecf99d5d0f · outbound

This paper cites an unresolved cited work.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:19:52.368949Z

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Observation ac2e7e29-794d-422a-8b4f-3de12e44456c · outbound

This paper cites A novel complex-valued gaussian measurement matrix for image compressed sensing.Entropy, 25(9):1248, 2023.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices A novel complex-valued gaussian measurement matrix for image compressed sensing.Entropy, 25(9):1248, 2023

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.352472Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 76814c57-c5e4-4bca-84a1-9f0970d91181 · outbound

This paper cites Learning a compressed sensing measurement matrix via gradient unrolling.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Learning a compressed sensing measurement matrix via gradient unrolling

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.336288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 31146108-adb5-4894-be34-073d7988503c · outbound

This paper cites Optimized projection matrix for compressive sensing.EURASIP Journal on Advances in Signal Processing, 2010:1–8, 2010.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Optimized projection matrix for compressive sensing.EURASIP Journal on Advances in Signal Processing, 2010:1–8, 2010

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.320174Z

Source-reported events for the cited work

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Observation 2c184d5e-c5c7-4f01-8a8b-bccfc40ae6b3 · outbound

This paper cites A new method of measurement matrix optimization for compressed sensing based on alternating minimization.Mathematics, 9(4):329, 2021.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices A new method of measurement matrix optimization for compressed sensing based on alternating minimization.Mathematics, 9(4):329, 2021

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.303342Z

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Observation 5324bfd9-27e9-4740-a365-7f385e76b304 · outbound

This paper cites Ista-net: Interpretable optimization-inspired deep network for image compressive sensing.

Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices Ista-net: Interpretable optimization-inspired deep network for image compressive sensing

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:19:52.287635Z

Source-reported events for the cited work

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