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Paper Citation Record · LEDGER

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging

As of 9 August 2026, this Paper Citation Record lists 100 of 138 outbound references and 0 inbound Pith citation observations for arXiv:2607.25967.

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

pith.paper-citation-record.v1
2607.25967 v1

Coverage vector

measured 100 of 138 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T01:05:09.532257Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

100 of 138 outbound references displayed

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  • verified fuzzy0
  • unresolved97
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fd0d0cd5-5dac-462a-923e-3217273bed90 · outbound

This paper cites Handbook for Automatic Computation: Linear Algebra , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Handbook for Automatic Computation: Linear Algebra , pages=

Reference 1

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source=arxiv_source observed=2026-08-01T01:05:09.191467Z digest=sha256:b5fb60ea2a3e75510546e38a8adaee8fdf8bec2b87173b1f28f0cc54e194ae33

Observation 77f11a62-d324-4c67-aa94-125d2eac24e3 · outbound

This paper cites 2013 , publisher=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2013 , publisher=

Reference 3

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Observation c9a18725-1abe-47b8-a85e-532fa98f1e2b · outbound

This paper cites SIAM journal on matrix analysis and applications , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging SIAM journal on matrix analysis and applications , volume=

Reference 4

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Observation 28aa0ed5-a1d7-4b33-a673-dd7d268b0873 · outbound

This paper cites SIAM journal on scientific computing , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging SIAM journal on scientific computing , volume=

Reference 5

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Observation af6e5f79-6681-4ee3-9442-bb565b935ce5 · outbound

This paper cites 2026 , url =.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2026 , url =

Reference 6

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Observation e631efc5-6d4b-4331-9834-c7c5d9234550 · outbound

This paper cites ACM Transactions on Knowledge Discovery from Data (TKDD) , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging ACM Transactions on Knowledge Discovery from Data (TKDD) , volume=

Reference 7

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Observation 01e63e42-2ebc-4cf4-8fbc-bec341549b83 · outbound

This paper cites Proceedings of the 2003 SIAM international conference on data mining , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the 2003 SIAM international conference on data mining , pages=

Reference 8

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Observation f32b6b4f-9f1f-4d29-8b8b-1134c3aff3e4 · outbound

This paper cites BIT Numerical Mathematics , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging BIT Numerical Mathematics , volume=

Reference 10

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Observation d45b0569-2a41-4618-8324-c8936f8d7447 · outbound

This paper cites SIAM Journal on Scientific and Statistical Computing , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging SIAM Journal on Scientific and Statistical Computing , volume=

Reference 11

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Observation 340ad15e-b016-46f3-adb3-41aa20b980b4 · outbound

This paper cites SIAM review , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging SIAM review , volume=

Reference 13

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Observation a423dc04-f8cb-4fe8-8582-3d141e885248 · outbound

This paper cites Linear Algebra and its Applications , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Linear Algebra and its Applications , volume=

Reference 14

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Observation 28333909-6da1-4fe1-adde-05d0b28674ef · outbound

This paper cites SIAM Journal on Scientific Computing , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging SIAM Journal on Scientific Computing , volume=

Reference 15

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Observation ce222821-e426-4796-9f5b-1736aea7e17d · outbound

This paper cites SIAM Journal on Computing , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging SIAM Journal on Computing , volume=

Reference 16

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Observation fd024ce7-a3f0-4682-8cef-cca2266c349d · outbound

This paper cites The random.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging The random

Reference 17

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Observation 7e193fbc-edcb-46e6-85d8-3d7ec4756ec3 · outbound

This paper cites JOSA A , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging JOSA A , volume=

Reference 18

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Observation 25c6c4d1-e086-450b-bac8-a052939b7861 · outbound

This paper cites Introduction and application , author=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Introduction and application , author=

Reference 19

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Observation a788f345-3338-4711-9541-15e42efe3dd6 · outbound

This paper cites 2009 IEEE international symposium on parallel & distributed processing , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2009 IEEE international symposium on parallel & distributed processing , pages=

Reference 20

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Observation 1454c4d4-049e-4a15-92e9-7b26091b0ddd · outbound

This paper cites IEEE Transactions on Information Theory , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging IEEE Transactions on Information Theory , volume=

Reference 21

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Observation e30c5bb5-c2c0-4ff1-a6d3-bfb55693ad11 · outbound

This paper cites The flexible tensor singular value decomposition and its applications in multisensor signal fusion processing , journal =.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging The flexible tensor singular value decomposition and its applications in multisensor signal fusion processing , journal =

Reference 22

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Observation 2f66bb16-3f9e-4bbd-99d6-f5e89d5290ec · outbound

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Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Tensor SVD: Statistical and Computational Limits , year=

Reference 23

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Observation b7198877-be72-4ba6-afe1-0833f185a6bd · outbound

This paper cites International Conference on Machine Learning , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging International Conference on Machine Learning , pages=

Reference 24

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Observation 54162b62-fcbe-48a3-9b34-0b445cd7214a · outbound

This paper cites International Conference on Machine Learning , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging International Conference on Machine Learning , pages=

Reference 25

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Observation f7aed72a-5575-4ddf-9e7c-bfba844a3796 · outbound

This paper cites Advances in neural information processing systems , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Advances in neural information processing systems , volume=

Reference 26

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Observation 7a87e1dd-5d49-48f7-993c-bbbe282ae757 · outbound

This paper cites LazySVD: Even Faster SVD Decomposition Yet Without Agonizing Pain , url =.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging LazySVD: Even Faster SVD Decomposition Yet Without Agonizing Pain , url =

Reference 28

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Observation 7229615c-685e-4c98-a24b-5269adf7fc21 · outbound

This paper cites IEEE Transactions on signal processing , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging IEEE Transactions on signal processing , volume=

Reference 29

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Observation 1a46476a-f705-468b-a317-8b9de6873f89 · outbound

This paper cites 2022 IEEE International Conference on Data Mining Workshops (ICDMW) , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2022 IEEE International Conference on Data Mining Workshops (ICDMW) , pages=

Reference 30

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This paper cites 7th International Conference on Learning Representations , year=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 7th International Conference on Learning Representations , year=

Reference 32

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Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2022 , editor =

Reference 33

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This paper cites Proceedings of the 31st ACM international conference on information & knowledge management , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the 31st ACM international conference on information & knowledge management , pages=

Reference 34

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Observation 77e5cd31-3527-4954-ae70-ff4e9716dcb7 · outbound

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Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging ACM Transactions on Intelligent Systems and Technology , volume=

Reference 35

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This paper cites PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence, Gold Coast, QLD, Australia, December 1-5, 2014.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging PRICAI 2014: Trends in Artificial Intelligence: 13th Pacific Rim International Conference on Artificial Intelligence, Gold Coast, QLD, Australia, December 1-5, 2014

Reference 36

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Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Singular Value Decomposition and Neural Networks

Reference 37

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Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2022 , isbn =

Reference 38

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Observation 8e7c82fa-897c-4bb6-96e8-743812e358ed · outbound

This paper cites Spatiotemporal Clutter Filtering of Ultrafast Ultrasound Data Highly Increases Doppler and fUltrasound Sensitivity , year=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Spatiotemporal Clutter Filtering of Ultrafast Ultrasound Data Highly Increases Doppler and fUltrasound Sensitivity , year=

Reference 39

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Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging IEEE transactions on medical imaging , volume=

Reference 40

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Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Nature Biomedical Engineering , volume=

Reference 41

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Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging doi:10.5281/zenodo.4343435 , url =

Reference 42

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Observation 31656fbf-6f67-4608-843d-fd5e43a74e82 · outbound

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Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2020 , note =

Reference 43

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source=arxiv_source observed=2026-08-01T01:05:09.328494Z digest=sha256:df465e5923d8501a8f0001531cbec1af4fdce4f306460d47a7d9b3054ece2536

Observation b0bbe47a-cc3c-4242-8aff-9fd47df458f3 · outbound

This paper cites Nature , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Nature , volume=

Reference 44

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source=arxiv_source observed=2026-08-01T01:05:09.331230Z digest=sha256:bb3f94962d1a786f4c49c830153516d1d1e93c474e6b3c3cb677e590387623eb

Observation 9f41b066-9ce1-410d-9494-8e0834757e95 · outbound

This paper cites RF-ULM: Ultrasound Localization Microscopy Learned From Radio-Frequency Wavefronts , year=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging RF-ULM: Ultrasound Localization Microscopy Learned From Radio-Frequency Wavefronts , year=

Reference 45

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source=arxiv_source observed=2026-08-01T01:05:09.334026Z digest=sha256:177821d3543c54f1549bf827daf95d586bc8afaf9459744bb71fcdf9cb9ab285

Observation 3ab1e96a-31d7-47f7-827f-cf8236e14bf2 · outbound

This paper cites Magnetic resonance in medicine , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Magnetic resonance in medicine , volume=

Reference 46

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source=arxiv_source observed=2026-08-01T01:05:09.336781Z digest=sha256:070c87a3a66ae362090eebedaabbc30189de3febe086b0b19e70e06ff1fc8fd1

Observation 631e663b-3fea-49ac-8fb7-60b452142393 · outbound

This paper cites an unresolved cited work.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Unresolved cited work

Reference 47

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source=arxiv_source observed=2026-08-01T01:05:09.339496Z digest=sha256:70def0aab905972206836b90f8b8fbd7d5258080a8184ca7c046888f01c64c7a

Observation 8bfee22f-4317-4ebd-91a0-e6145d7a6797 · outbound

This paper cites International journal of computer assisted radiology and surgery , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging International journal of computer assisted radiology and surgery , pages=

Reference 48

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no resolver link, observed 2026-08-01T01:05:09.342952Z

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source=arxiv_source observed=2026-08-01T01:05:09.342952Z digest=sha256:0d5b6dc9cdffbb1573ba5817fa9e6eec92c3fb364fd09fca21c303ed0479c392

Observation 580bfe9d-0b73-4006-936b-eeda92f77603 · outbound

This paper cites Polarimetric feature analysis of Mueller matrices for brain tumor image segmentation , volume =.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Polarimetric feature analysis of Mueller matrices for brain tumor image segmentation , volume =

Reference 49

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no resolver link, observed 2026-08-01T01:05:09.345519Z

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source=arxiv_source observed=2026-08-01T01:05:09.345519Z digest=sha256:57f1e783dc3a3bdc0539bb02909aecfc685c9eb8a820a078b3c5ea6a839843d4

Observation 7d8bd757-2566-4a38-a727-fb9c2a85b5da · outbound

This paper cites Physically Consistent Image Augmentation for Deep Learning in Mueller Matrix Polarimetry , year=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Physically Consistent Image Augmentation for Deep Learning in Mueller Matrix Polarimetry , year=

Reference 50

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source=arxiv_source observed=2026-08-01T01:05:09.348178Z digest=sha256:2db93c84a4a1fc56938ec714a72efc78ad399a46cfb833bf3f3bc9c162e98078

Observation 47f36ccf-9d4e-4142-b99f-e3e33e91fb5b · outbound

This paper cites IEEE Photonics Journal , year=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging IEEE Photonics Journal , year=

Reference 51

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source=arxiv_source observed=2026-08-01T01:05:09.351527Z digest=sha256:bfe4580cefea10a3674591629d4eee7c44bc5ccd820868db4fe88a8dc35b994d

Observation 1aa423c4-8f85-4f30-b548-cd8d71ef789d · outbound

This paper cites Artificial Intelligence Review , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Artificial Intelligence Review , volume=

Reference 52

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source=arxiv_source observed=2026-08-01T01:05:09.354396Z digest=sha256:71ea6d61bd04f93f88eeb281f19e7b2682fd291a071520c4abe31915f41437fc

Observation 6155face-ef61-4a99-81fe-54a3450f19b5 · outbound

This paper cites IEEE Transactions on Image Processing , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging IEEE Transactions on Image Processing , volume=

Reference 53

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no resolver link, observed 2026-08-01T01:05:09.357110Z

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source=arxiv_source observed=2026-08-01T01:05:09.357110Z digest=sha256:f8d4af4f0da8fd3ba3d9ac53b86d444de319e942927877895e681c3e105b53e4

Observation bf3a6131-7b3e-4557-99a2-aea93e121fa5 · outbound

This paper cites an unresolved cited work.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Unresolved cited work

Reference 54

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source=arxiv_source observed=2026-08-01T01:05:09.359945Z digest=sha256:b5904f9e0258f332e25f768c48eb28c72f9c93e6b31d005c90d503607815331f

Observation d8a05685-66f7-4d4a-86fb-95189875da80 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 55

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source=arxiv_source observed=2026-08-01T01:05:09.362892Z digest=sha256:70a27b0e7a6124050952ab97f4cea9bab45ed0080bb71d1b64706f381c6066d0

Observation c6a5604a-f8f0-4fc8-8dba-b9f78385a492 · outbound

This paper cites Proceedings of the Computer Vision and Pattern Recognition Conference , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

Reference 56

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no resolver link, observed 2026-08-01T01:05:09.365717Z

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source=arxiv_source observed=2026-08-01T01:05:09.365717Z digest=sha256:e3e1f0264ce0213d1c3575e14c7247f8a6648115f385e84a8afa276080e96bf6

Observation 6b78b0f4-d3a2-4a2f-b623-d1832a59b829 · outbound

This paper cites Robotics: science and systems , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Robotics: science and systems , volume=

Reference 57

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source=arxiv_source observed=2026-08-01T01:05:09.368318Z digest=sha256:078163d8b714052b040c46336b86d3824ddb492a34ca9854568a8317cd694a55

Observation 661902a4-8a3e-4add-9d33-486d181a0bcb · outbound

This paper cites 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pages=

Reference 58

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source=arxiv_source observed=2026-08-01T01:05:09.370855Z digest=sha256:c632707691a1c4a67ffaee5c91d1207d74ff04f28b4fe52f78bf86b020a76739

Observation fdf54c0c-d298-4321-8f38-e37248f44f56 · outbound

This paper cites International journal of computer assisted radiology and surgery , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging International journal of computer assisted radiology and surgery , volume=

Reference 59

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no resolver link, observed 2026-08-01T01:05:09.373495Z

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source=arxiv_source observed=2026-08-01T01:05:09.373495Z digest=sha256:78724281092bd175f2d788e1b26b34f51db097b846460deec9dade2b132b6441

Observation 34a6e429-dbc0-4073-b890-77c13b7f1bec · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 60

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no resolver link, observed 2026-08-01T01:05:09.376128Z

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source=arxiv_source observed=2026-08-01T01:05:09.376128Z digest=sha256:82eb8736bc4ca8bde63a4492ccba7d345ab50d5a1587306830a4543f86dc2e2f

Observation f4a4887b-acde-4655-b844-f78cdd5e6eca · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

Reference 61

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source=arxiv_source observed=2026-08-01T01:05:09.379065Z digest=sha256:b262062e1fe705277aa816001d71bce74b2e0c081a01611945880f57b8350ad6

Observation 8871c422-f09a-4288-8277-48443d0e2c11 · outbound

This paper cites an unresolved cited work.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Unresolved cited work

Reference 62

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source=arxiv_source observed=2026-08-01T01:05:09.382233Z digest=sha256:58ca0d9f1bcf576f59d6e54e76104d7eb72748246f70b144e83c00bc60c215e6

Observation 77d2abda-bcb3-4779-b700-d1144ac35884 · outbound

This paper cites 2023 , url =.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2023 , url =

Reference 63

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source=arxiv_source observed=2026-08-01T01:05:09.385321Z digest=sha256:21892b10d01da80eb558ef7c05f9e01f0b3d923c6fd3d78c216766927ba145cc

Observation 5c956584-12e9-475a-990e-29c8ac6d239a · outbound

This paper cites SIAM review , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging SIAM review , volume=

Reference 64

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source=arxiv_source observed=2026-08-01T01:05:09.388086Z digest=sha256:7ef3eb94ecc3058867e61e7d40fea1d19449693b3bdb1e39b78a04766c42539c

Observation 9d8c3247-e2c9-49a8-a9a0-a8d0c82bd5c0 · outbound

This paper cites SIAM Review , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging SIAM Review , volume=

Reference 65

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source=arxiv_source observed=2026-08-01T01:05:09.392218Z digest=sha256:35c8228de4f8744a8c5e0833af61aa5c836ec385ef37298b7a77c76d8d03348e

Observation 2900d77c-4a6a-49ad-9934-c102feec2049 · outbound

This paper cites Dai , keywords =.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Dai , keywords =

Reference 66

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verified exact
doi, observed 2026-08-01T01:05:52.516311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-01T01:05:09.395166Z digest=sha256:f6c6bd7dfa75331cd9345ab9e44c8d8ac4e9a9cee508152986e2a1adeef6a7e3

Observation bac54c7f-1d73-47e4-8ff0-ec53f79a02c8 · outbound

This paper cites Scientific Data , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Scientific Data , volume=

Reference 68

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source=arxiv_source observed=2026-08-01T01:05:09.401545Z digest=sha256:8f3a74009fcbb945cea65bd284f1a9002b0ceab26c43e392e8690209e8fd6c78

Observation 5d1f1abb-3f47-4f3d-b76c-ca0c5f516bca · outbound

This paper cites Radiology: Artificial Intelligence , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Radiology: Artificial Intelligence , volume=

Reference 69

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no resolver link, observed 2026-08-01T01:05:09.404163Z

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source=arxiv_source observed=2026-08-01T01:05:09.404163Z digest=sha256:6ed6fe1273c2d8626dc168cdeedb940bf80f79bb76e4bcc2f86163ba80323b3a

Observation 35242cfd-c391-43ec-aa9a-967c4b344deb · outbound

This paper cites Acm Sigkdd Explorations Newsletter , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Acm Sigkdd Explorations Newsletter , volume=

Reference 70

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source=arxiv_source observed=2026-08-01T01:05:09.406918Z digest=sha256:c14815dfa518d5dec1e4b0d11bb769d437b8e4032fded7265ed57beea5da7920

Observation af02afb9-9b0a-4545-b97e-6cc9060751a5 · outbound

This paper cites Proceedings of KDD Cup 2011 , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of KDD Cup 2011 , pages=

Reference 71

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source=arxiv_source observed=2026-08-01T01:05:09.410183Z digest=sha256:3a06f5c97b5d12565ab71c037fe4c8ab135b9fcf70f8891005153649165434aa

Observation edcefdce-ba61-400b-b45b-c03103113270 · outbound

This paper cites http://yann.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging http://yann

Reference 72

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source=arxiv_source observed=2026-08-01T01:05:09.413708Z digest=sha256:ea56d42477a4ccb6f11905dfe577c33f2b287fb2fab903e31bfb8d212ff992ba

Observation e5ccda02-3e5c-401c-b9e9-e2bc05d3a16f · outbound

This paper cites Nucleic acids research , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Nucleic acids research , volume=

Reference 73

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source=arxiv_source observed=2026-08-01T01:05:09.416630Z digest=sha256:7cfbe43838aba3147e93978fb94cf29a4f167045140511583d49dc634ef82dd3

Observation d6ef20bb-cf17-469d-af0f-bea494bb3c9c · outbound

This paper cites 2010 IEEE International conference on data mining , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2010 IEEE International conference on data mining , pages=

Reference 74

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source=arxiv_source observed=2026-08-01T01:05:09.419767Z digest=sha256:188ad8bb6057bc3a109616a6a22253811fecbd8d151e248ea6da053380ae4940

Observation d6042442-c930-454d-9d80-fba095157a86 · outbound

This paper cites Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval , pages=

Reference 75

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source=arxiv_source observed=2026-08-01T01:05:09.422525Z digest=sha256:0d9139048695a54336df27ca87bf54a299b917a94721c14175a8d77112f2a84a

Observation 469a0a54-f258-4151-ab61-bfa64c0b260b · outbound

This paper cites Advances in neural information processing systems , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Advances in neural information processing systems , volume=

Reference 76

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source=arxiv_source observed=2026-08-01T01:05:09.425308Z digest=sha256:789124139d2b0c4d520660b510fd554f9dbf63c75836a28638b4c1085a5dc509

Observation 079afe6f-700a-479f-bfdc-c49842cc1e4e · outbound

This paper cites Advances in Applied Clifford Algebras , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Advances in Applied Clifford Algebras , volume=

Reference 77

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source=arxiv_source observed=2026-08-01T01:05:09.428208Z digest=sha256:b0c9826c7fa8012a4c6713b5bf46a32ab4bc23fa0982f2b8630ae5e09f45dd4d

Observation 12fc6357-084f-4855-a56c-a9c693512a7f · outbound

This paper cites Proceedings of the 36th International Conference on Machine Learning , pages =.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the 36th International Conference on Machine Learning , pages =

Reference 78

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source=arxiv_source observed=2026-08-01T01:05:09.431157Z digest=sha256:275fc6827164042f1fc010e1c3cfa51d788aafd4afb19fd1d8d35e82fa93810f

Observation 330138c0-7358-4f0d-8330-2757d862cfff · outbound

This paper cites International Conference on Machine Learning , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging International Conference on Machine Learning , pages=

Reference 79

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no resolver link, observed 2026-08-01T01:05:09.434660Z

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source=arxiv_source observed=2026-08-01T01:05:09.434660Z digest=sha256:d799af77fc8b7619df4f632f966af1fecaf2c108de646affea0e6c44d88beac8

Observation 08adca26-b528-4909-8af2-4fbe3395cc95 · outbound

This paper cites 2023 , urldate =.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2023 , urldate =

Reference 81

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source=arxiv_source observed=2026-08-01T01:05:09.441288Z digest=sha256:dbdd5c1369800d1fe349a5d221fcc77063dadac0674d71c3ef1a2dfcdae8cac3

Observation 0c0c414a-a999-4566-aa05-8f60fac18db9 · outbound

This paper cites 2021 , urldate =.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2021 , urldate =

Reference 82

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source=arxiv_source observed=2026-08-01T01:05:09.445122Z digest=sha256:29d7922a19b6561d7b7ecc5a8341d5000084826205e62a46dd8714ce3b4cee6d

Observation 22958f0b-6e4c-4e97-a602-5301862ad9cd · outbound

This paper cites 2024 , urldate =.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging 2024 , urldate =

Reference 84

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source=arxiv_source observed=2026-08-01T01:05:09.451211Z digest=sha256:9ae4bea09dff9a533335dec31177385eea2f3895d9c66475a4bca97647834f3a

Observation e025a9da-a57b-4137-8376-2fbf6dad4b4b · outbound

This paper cites Communications of the ACM , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Communications of the ACM , volume=

Reference 87

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source=arxiv_source observed=2026-08-01T01:05:09.461117Z digest=sha256:d8e8f65e86f04b657a9f4fda40f786102bf39bc6ffc4f4d35778af2d19a40e01

Observation 7036fb6e-3331-4602-afb3-3ae3d8921b06 · outbound

This paper cites Journal of the ACM (JACM) , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Journal of the ACM (JACM) , volume=

Reference 88

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Observation 052b316a-ee06-4545-96b4-df8d03b28512 · outbound

This paper cites Mathematics , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Mathematics , volume=

Reference 89

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Observation 6be5ffd6-b245-4650-b62c-77f1f924e4fc · outbound

This paper cites SIAM Journal on Scientific Computing , volume =.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging SIAM Journal on Scientific Computing , volume =

Reference 90

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Observation 99357f14-1d8e-48f1-8871-9fc1f310dca0 · outbound

This paper cites European Conference on Computer Vision , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging European Conference on Computer Vision , pages=

Reference 91

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source=arxiv_source observed=2026-08-01T01:05:09.474235Z digest=sha256:f264074356e45d06c451b6f600e345aa428b69ff9e1c309c7bf1250d54dbf696

Observation 47664071-d19d-4f8c-bd0f-b759f1c9dde0 · outbound

This paper cites Biomedical engineering online , volume=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Biomedical engineering online , volume=

Reference 92

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source=arxiv_source observed=2026-08-01T01:05:09.477180Z digest=sha256:7f6bf82d962ae655bb1911cfd028da41a0e72d37ae206c7329e020b84070ac7b

Observation 9b44d472-013d-41a2-9d6c-9c737ffb723a · outbound

This paper cites Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging Problems.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Differentiable SVD based on Moore-Penrose Pseudoinverse for Inverse Imaging Problems

Reference 93

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source=arxiv_source observed=2026-08-01T01:05:09.480213Z digest=sha256:91d9013aaf8cf7fc2973192f433428006047db881da1b6fb42c7016c231f95f1

Observation 2a82ea3a-d42b-496f-b476-42911d605422 · outbound

This paper cites Proceedings of the IEEE international conference on computer vision , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the IEEE international conference on computer vision , pages=

Reference 94

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source=arxiv_source observed=2026-08-01T01:05:09.483718Z digest=sha256:8d35a47c7819c570045c3d3144fd273207609d53c867981a2f585ff34542b3ab

Observation e04d0284-d7d7-41ab-9634-c907d81262b2 · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 95

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source=arxiv_source observed=2026-08-01T01:05:09.486997Z digest=sha256:28f22613faaffe70978b285f843cadaecc206c0e699e01bdc3180bbd86663af1

Observation 0022e437-e28d-45e7-826e-faf2f9be72ae · outbound

This paper cites Proceedings of the IEEE international conference on computer vision workshops , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the IEEE international conference on computer vision workshops , pages=

Reference 96

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source=arxiv_source observed=2026-08-01T01:05:09.489814Z digest=sha256:75aefaceb5876120b379bb1e5771c1fb7a041764d63295738090781c4645557c

Observation 5d31cc8d-954c-4fe7-b056-731e100c13bb · outbound

This paper cites Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages=

Reference 97

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source=arxiv_source observed=2026-08-01T01:05:09.493068Z digest=sha256:58bad3b1ff9ea6da4e84d2d26500470e1d68132c8d32ac60e1f4e738c9db0e83

Observation 2a40ec3c-4edd-4b88-828e-42a166c73310 · outbound

This paper cites Proceedings of the Computer Vision and Pattern Recognition Conference , pages=.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

Reference 98

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source=arxiv_source observed=2026-08-01T01:05:09.495751Z digest=sha256:e2f740d57c29a6fd9710942c75d4bbfc4a3284459f52cf9ac4fa0a97113b0a0b

Observation d921c08a-d399-4007-9d15-55bcb58b5fb1 · outbound

This paper cites K-svd: An algorithm for designing overcomplete dictionaries for sparse representation.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging K-svd: An algorithm for designing overcomplete dictionaries for sparse representation

Reference 99

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source=arxiv_source observed=2026-08-01T01:05:09.498916Z digest=sha256:c7f3c981a03a787e3616b0ad6a550ffc6f4bfe6e104fa1e160254fee298ace20

Observation a98d73c0-81c6-490c-9288-c1df831b2d06 · outbound

This paper cites Adaptive spatiotemporal svd clutter filtering for ultrafast doppler imaging using similarity of spatial singular vectors.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Adaptive spatiotemporal svd clutter filtering for ultrafast doppler imaging using similarity of spatial singular vectors

Reference 100

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source=arxiv_source observed=2026-08-01T01:05:09.501857Z digest=sha256:b80c4a158a16e991856c0e8bbd167fae0c130f2f47e5f27e3fb21ba90580a478

Observation a9b9b386-f9a2-41a1-b291-57f9e9926fd3 · outbound

This paper cites Programming parallel algorithms.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Programming parallel algorithms

Reference 101

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source=arxiv_source observed=2026-08-01T01:05:09.505151Z digest=sha256:abb474bf9a67bbbf8ced746d6bf2a5d368b95094139c31c6de8a06bf00d03c43

Observation d46a3b7b-e3d4-4921-8eb9-392bdb0478bb · outbound

This paper cites Fast low-rank modifications of the thin singular value decomposition.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Fast low-rank modifications of the thin singular value decomposition

Reference 102

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source=arxiv_source observed=2026-08-01T01:05:09.508218Z digest=sha256:4c016dc45d12bc6203653edc6a98abf15e6c278e5a398a25b5a218689f7c8bf1

Observation 6cb9692c-27e3-4957-9fee-1886df3bb8ff · outbound

This paper cites The parallel evaluation of general arithmetic expressions.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging The parallel evaluation of general arithmetic expressions

Reference 103

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source=arxiv_source observed=2026-08-01T01:05:09.510968Z digest=sha256:14f4485d018d4c9403a49cd5c88627ad9659648af93f926e072c0ba4f06ddf71

Observation db709339-de62-4735-8590-a9fc92dd4130 · outbound

This paper cites Rank revealing qr factorizations.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Rank revealing qr factorizations

Reference 104

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source=arxiv_source observed=2026-08-01T01:05:09.513890Z digest=sha256:cdae80db469c57dd44db4943506646fb709211fc85f9f362ae6f88e8706b0952

Observation ff945847-8297-47b9-911f-e305286b1d1d · outbound

This paper cites Learned compression of high dimensional image datasets.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Learned compression of high dimensional image datasets

Reference 105

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source=arxiv_source observed=2026-08-01T01:05:09.516670Z digest=sha256:598ccc0ebb9348e4eeb979e632b61072315a70ff58526e2a69f92137f244d3ff

Observation c89c6181-f1f5-4f72-82fc-0e559b0093e2 · outbound

This paper cites Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Spatiotemporal clutter filtering of ultrafast ultrasound data highly increases doppler and fultrasound sensitivity

Reference 106

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source=arxiv_source observed=2026-08-01T01:05:09.519415Z digest=sha256:60f52f9e653ce4585cc931edcd887497ce26ac78eb4cf11e4c1dc07657ae5a8f

Observation 35595dfd-3aa2-4c40-8fc5-40551dfb961d · outbound

This paper cites Jacobi’s method is more accurate than qr.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Jacobi’s method is more accurate than qr

Reference 107

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source=arxiv_source observed=2026-08-01T01:05:09.522241Z digest=sha256:d9a58719ac1193c2199a319a4f8e19bb006d5ef66c5bf206041ba18ff4d8d986

Observation 62630894-c375-4e71-9b61-ec2ad3d0b778 · outbound

This paper cites Fast monte carlo algorithms for matrices ii: Computing a low-rank approximation to a matrix.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Fast monte carlo algorithms for matrices ii: Computing a low-rank approximation to a matrix

Reference 108

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source=arxiv_source observed=2026-08-01T01:05:09.525447Z digest=sha256:e9a950e37b63d5806e30243142b181170d24f6f3cba5c5be4b7ad459a4814e44

Observation db9f8d14-8250-448f-aef7-129de9dae77f · outbound

This paper cites Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Ultrafast ultrasound localization microscopy for deep super-resolution vascular imaging

Reference 109

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source=arxiv_source observed=2026-08-01T01:05:09.528453Z digest=sha256:37bfdd0684b27aed6c63feedaae8635a92c09cec128fec4a013c2811a18b5f84

Observation 3dd6d375-b8c6-402e-8bab-cd1a1fb1d4e6 · outbound

This paper cites Gated recurrence enables simple and accurate sequence prediction in stochastic, changing, and structured environments.

Quasi-SVD: Learning a Lie-constrained matrix factorisation for real-time imaging Gated recurrence enables simple and accurate sequence prediction in stochastic, changing, and structured environments

Reference 110

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verified exact
doi, observed 2026-08-01T01:05:51.910369Z

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source=arxiv_source observed=2026-08-01T01:05:09.532257Z digest=sha256:b82cc33577a9726612b514ac5e4e14f9f7c20c4988be23d354dc2a1f5a1afe1b

Pith citing papers

No inbound Pith citation observations are available.