Paper Citation Record · LEDGER

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis

As of 8 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.05617.

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

pith.paper-citation-record.v1
2506.05617 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:19:40.798356Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

29 of 29 outbound references displayed

verified exact0
verified fuzzy27
unresolved2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 59b45c5d-5eb7-4a5f-b92b-53dac0bb5854 · outbound

This paper cites ImageNet Large Scale Visual Recognition Chall enge,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis ImageNet Large Scale Visual Recognition Chall enge,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:44.441013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cbc24a28-aaf4-4ed1-a17f-8ea4b35cd98e · outbound

This paper cites ImageNet Classiﬁcation with Deep Convolutional Neural Networks,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis ImageNet Classiﬁcation with Deep Convolutional Neural Networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:44.266300Z

Source-reported events for the cited work

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Observation ab633c0b-b86b-4b9d-8075-83d3d0935f9c · outbound

This paper cites Deep Residual Learnin g for Image Recognition,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Deep Residual Learnin g for Image Recognition,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:44.084392Z

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Observation 51c9e178-dfde-4fe0-8247-4b406e24e232 · outbound

This paper cites Gradient-based learning applied to document recognition,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Gradient-based learning applied to document recognition,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:43.897570Z

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Observation 140bb697-ce06-4338-ae57-ca0d727e3986 · outbound

This paper cites Spectral norm regularization for improving the generalizability of deep learning,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Spectral norm regularization for improving the generalizability of deep learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:43.721150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 293c7ec1-650d-45f8-8812-6e51d827ac3a · outbound

This paper cites The singular values o f convolu- tional layers,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis The singular values o f convolu- tional layers,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:43.560682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9eb07678-f70e-477d-848d-de0ea5c801b6 · outbound

This paper cites Regular isation of neural networks by enforcing lipschitz continuity,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Regular isation of neural networks by enforcing lipschitz continuity,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:43.393383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6ad32d9f-4380-4af9-9e0c-e2b113b0517b · outbound

This paper cites Parseval networks: improving robustness to adversarial e xamples,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Parseval networks: improving robustness to adversarial e xamples,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:43.193350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 48fd1190-ae93-4bc3-9753-a6d3065908ab · outbound

This paper cites Invisible back door attack through singular value decomposition,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Invisible back door attack through singular value decomposition,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:43.051342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 73e3272a-ae2b-4b35-81cc-f68319dd4f40 · outbound

This paper cites Speeding u p convolutional neural networks with low rank expansions,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Speeding u p convolutional neural networks with low rank expansions,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:42.868546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0d2c146d-c4fc-46fd-9012-a728ad47a79f · outbound

This paper cites Accelerating very de ep convolu- tional networks for classiﬁcation and detection,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Accelerating very de ep convolu- tional networks for classiﬁcation and detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:42.650932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4838274c-823e-4ca2-a0d3-fd31784bc5dd · outbound

This paper cites Compressing pre-trained language models by matrix decomposition,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Compressing pre-trained language models by matrix decomposition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:42.472784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 835d9827-9e78-4c96-b29a-b8a6995fb88b · outbound

This paper cites GroupR educe: Block- wise low-rank approximation for neural language model shri nking,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis GroupR educe: Block- wise low-rank approximation for neural language model shri nking,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:42.324857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7baf2db3-b1fe-462a-8a6a-6df4fcf058e7 · outbound

This paper cites Onl ine embedding compression for text classiﬁcation using low rank matrix fa ctorization,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Onl ine embedding compression for text classiﬁcation using low rank matrix fa ctorization,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:42.199269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 27d2e0e0-9d16-4fc9-9f20-34795e71bf53 · outbound

This paper cites L anguage model compression with weighted low-rank factorization,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis L anguage model compression with weighted low-rank factorization,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:42.069319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation afe5a595-7980-4ae0-bf58-4a20232ef1d4 · outbound

This paper cites The SVD of Convolutional Weights: A CNN Interpretability Framework.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis The SVD of Convolutional Weights: A CNN Interpretability Framework

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:39.626185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e71ff428-9911-4130-8430-cb2d410e462a · outbound

This paper cites Automated local fourier anal ysis (aLFA),.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Automated local fourier anal ysis (aLFA),

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:41.933265Z

Source-reported events for the cited work

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Observation 2faa43ee-9e8c-4eb6-ae2e-bb1ece27775c · outbound

This paper cites MgNet: A uniﬁed framework of multigrid a nd convolutional neural network,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis MgNet: A uniﬁed framework of multigrid a nd convolutional neural network,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:41.849098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d509072e-4722-4def-a442-5f619f5d53a9 · outbound

This paper cites M gic: Multigrid- in-channels neural network architectures,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis M gic: Multigrid- in-channels neural network architectures,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:41.752780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 143fe1e3-7664-4d70-99fe-4951bdd768ff · outbound

This paper cites Mgiad: Multi grid in all dimensions. efﬁciency and robustness by weight sharing and coars- ening in resolution and channel dimensions,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Mgiad: Multi grid in all dimensions. efﬁciency and robustness by weight sharing and coars- ening in resolution and channel dimensions,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:41.638135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation cd611d09-3b13-4bdf-9d54-d28909455c84 · outbound

This paper cites Poly-mgnet: Polynomial building blocks in multigrid-inspired resnets,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Poly-mgnet: Polynomial building blocks in multigrid-inspired resnets,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:41.466201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e0f0809a-6014-4e54-9a26-3f9c67a84db8 · outbound

This paper cites Lipschitz-margi n training: Scal- able certiﬁcation of perturbation invariance for deep neur al networks,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Lipschitz-margi n training: Scal- able certiﬁcation of perturbation invariance for deep neur al networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:41.389919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1bdcaa11-0e77-4ace-884a-96673f915bdf · outbound

This paper cites A PAC-ba yesian ap- proach to spectrally-normalized margin bounds for neural n etworks,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis A PAC-ba yesian ap- proach to spectrally-normalized margin bounds for neural n etworks,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:41.301323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 682ba085-422c-4504-9e30-425de6256a3c · outbound

This paper cites Fantastic four: Differentiabl e and efﬁcient bounds on singular values of convolution layers,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Fantastic four: Differentiabl e and efﬁcient bounds on singular values of convolution layers,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:41.209211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4946ee0f-1c93-473f-874b-6320ca523a34 · outbound

This paper cites Plug -and- play methods provably converge with properly trained denoi sers,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Plug -and- play methods provably converge with properly trained denoi sers,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:41.162037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation abe366dc-665e-4e9e-9f88-16d10609e0d0 · outbound

This paper cites Exploiting linear structure within convolutional networks for efﬁcie nt evaluation,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Exploiting linear structure within convolutional networks for efﬁcie nt evaluation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:41.114398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 33e27dc2-e7a5-43af-8e81-66436bcb5906 · outbound

This paper cites Pseudoinvertible Neur al Networks ,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Pseudoinvertible Neur al Networks ,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:41.079084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5780c025-c74a-4fc8-9261-e12f7a36b48f · outbound

This paper cites an unresolved cited work.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:19:41.043426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 66512df7-6b97-4845-bd20-44f27df0fb25 · outbound

This paper cites Array programming with NumPy,.

LFA applied to CNNs: Efficient Singular Value Decomposition of Convolutional Mappings by Local Fourier Analysis Array programming with NumPy,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:19:40.948498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

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