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Paper Citation Record · LEDGER

Fast Training of Convolutional Networks through FFTs

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1312.5851.

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

pith.paper-citation-record.v1
1312.5851 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:30:18.593351Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T12:21:06.931158Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cde9db86-e55b-4cca-927b-e165f0e8abb6 · inbound

Symmetry group factorization reveals the structure-function relation in the neural connectome of Caenorhabditis elegans cites this paper.

Symmetry group factorization reveals the structure-function relation in the neural connectome of Caenorhabditis elegans Fast Training of Convolutional Networks through FFTs

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-14T10:37:21.589228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T10:37:21.589228Z digest=sha256:b06f37c6e6bbfd06edec693489c161bca16532f8711828b454278e1e169a1504

Observation 039d4433-1c76-4cdf-86ea-31d9b53cc7bf · inbound

Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence cites this paper.

Fourier analysis of the physics of transfer learning for data-driven subgrid-scale models of ocean turbulence Fast Training of Convolutional Networks through FFTs

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-16T11:30:18.593351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T11:30:18.593351Z digest=sha256:0fac945831eb9cb07a71ee75ca6ba6d40c863f7fef5138951b72283e7f03ab31

Observation ae0e94c4-32ed-442b-92bb-7396977f65d6 · inbound

Simple Graph Contrastive Learning via Fractional-order Neural Diffusion Networks cites this paper.

Simple Graph Contrastive Learning via Fractional-order Neural Diffusion Networks Fast Training of Convolutional Networks through FFTs

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-16T11:04:03.655332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:04:03.655332Z digest=sha256:610aaeb5eefeb541cfa7187921770c02729b01f488955fe0dfb95db464256931

Observation 952fc666-c5be-43b6-be37-ffbb7b3a661a · inbound

FPGA-based Acceleration for Convolutional Neural Networks: A Comprehensive Review cites this paper.

FPGA-based Acceleration for Convolutional Neural Networks: A Comprehensive Review Fast Training of Convolutional Networks through FFTs

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-16T04:09:50.855906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:09:50.855906Z digest=sha256:46c6e13b7e60d009e7bf176629af73cd2d6accd1e25a4d80ff4410df0eb746cc

Observation ed45d1ed-d695-4ad0-8b93-f3a74a05bf69 · inbound

Demystifying the 7-D Convolution Loop Nest for Data and Instruction Streaming in Reconfigurable AI Accelerators cites this paper.

Demystifying the 7-D Convolution Loop Nest for Data and Instruction Streaming in Reconfigurable AI Accelerators Fast Training of Convolutional Networks through FFTs

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-15T17:48:51.428344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:48:51.428344Z digest=sha256:a8f95d024f5c59753ada9bfa6dcc54052a73bbd28055d1160e4061c63f02f44e

Observation ddd86578-d8ac-47dd-8dd0-d7152d786e5e · inbound

Latent Fourier Transform cites this paper.

Latent Fourier Transform Fast Training of Convolutional Networks through FFTs

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-07-04T19:59:44.768319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-10T03:45:07.892234Z digest=sha256:10fd249a9ae1a22095a475e1f446dc138400246be0454648dd05e3dfd8c631ef

Observation 5fc96f72-c175-4b15-b42c-3e170a175d0a · inbound

Flash EQ-Linear: Accelerating Equivariant Linear Layers via Group-wise Discrete Fourier Transform cites this paper.

Flash EQ-Linear: Accelerating Equivariant Linear Layers via Group-wise Discrete Fourier Transform Fast Training of Convolutional Networks through FFTs

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-01T08:02:59.666834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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