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Paper Citation Record · LEDGER

On the Relationship between Self-Attention and Convolutional Layers

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:1911.03584.

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

pith.paper-citation-record.v1
1911.03584 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:08:52.398133Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:59:20.814270Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d67f4de8-724e-42a0-8e75-9b6d073ea9b3 · inbound

DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory cites this paper.

DragNUWA: Fine-grained Control in Video Generation by Integrating Text, Image, and Trajectory On the Relationship between Self-Attention and Convolutional Layers

Reference 163

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verified exact
arxiv_id, observed 2026-05-20T13:03:58.158726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-20T13:03:57.828598Z digest=sha256:656702e708d1990a96ab1297f819b195d95e6c17dd0ee8b23db9d5dc1f591671

Observation 46c93432-43c5-4786-839b-f71829323d37 · inbound

Exploring Visual Embedding Spaces Induced by Vision Transformers for Online Auto Parts Marketplaces cites this paper.

Exploring Visual Embedding Spaces Induced by Vision Transformers for Online Auto Parts Marketplaces On the Relationship between Self-Attention and Convolutional Layers

Reference 6

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no resolver link, observed 2026-08-08T18:08:52.398133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T18:08:52.398133Z digest=sha256:8b3392f0058e43999f2736943b7d975fa437048416014e5e8af22b6d07d9b7ca

Observation aeefb886-3e16-46c8-8d22-34be6485a512 · inbound

Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis cites this paper.

Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis On the Relationship between Self-Attention and Convolutional Layers

Reference 27

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no resolver link, observed 2026-08-08T13:18:18.993238Z

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Observation ac0b69bd-302a-4dc0-a399-65df8c2bfe8f · inbound

IAFormer: Interaction-Aware Transformer network for collider data analysis cites this paper.

IAFormer: Interaction-Aware Transformer network for collider data analysis On the Relationship between Self-Attention and Convolutional Layers

Reference 71

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verified exact
arxiv_id, observed 2026-05-22T17:14:59.586309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-22T17:13:47.293753Z digest=sha256:e73deb7460dd1ec3e1cc5c406c5c7f496e342ab4f000d8e53e2af115f7ad9bbc

Observation b859d881-c1fe-4521-a9c6-b078f1ef9c00 · inbound

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions cites this paper.

Numerical Investigation of Sequence Modeling Theory using Controllable Memory Functions On the Relationship between Self-Attention and Convolutional Layers

Reference 8

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no resolver link, observed 2026-08-07T10:22:22.158470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T10:22:22.158470Z digest=sha256:225070aa30977626e839f0a9c417fef170dfa93c531c49f56a06d965f2234d0d

Observation 4caa92a1-c886-42a6-8d9c-3b9712fd28f2 · inbound

Efficient Feedback Gate Network for Hyperspectral Image Super-Resolution cites this paper.

Efficient Feedback Gate Network for Hyperspectral Image Super-Resolution On the Relationship between Self-Attention and Convolutional Layers

Reference 32

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no resolver link, observed 2026-08-06T23:41:00.620976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0fc6cdd0-6d73-4d66-a852-b0e85994817c · inbound

Low-latency vision transformers via large-scale multi-head attention cites this paper.

Low-latency vision transformers via large-scale multi-head attention On the Relationship between Self-Attention and Convolutional Layers

Reference 11

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no resolver link, observed 2026-08-06T21:39:34.141455Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:39:34.141455Z digest=sha256:63c3ee6c0d8778e5c53544ab10a6516768f08e6b8e23c7aaa1fedc7fa9eb3ed4

Observation 9bbb0a99-a740-43d8-af83-abfb3903a314 · inbound

Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs cites this paper.

Graph Collaborative Attention Network for Link Prediction in Knowledge Graphs On the Relationship between Self-Attention and Convolutional Layers

Reference 7

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no resolver link, observed 2026-08-06T20:04:02.100755Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:04:02.100755Z digest=sha256:7610d61689abe7e504af011904651ac31a0dc78f0a17fc2ee4c28c50fe43f997

Observation 174c8a16-08bb-4748-8f16-b7f336d65cc6 · inbound

Compute Can't Handle the Truth: Why Communication Tax Prioritizes Memory and Interconnects in Modern AI Infrastructure cites this paper.

Compute Can't Handle the Truth: Why Communication Tax Prioritizes Memory and Interconnects in Modern AI Infrastructure On the Relationship between Self-Attention and Convolutional Layers

Reference 158

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no resolver link, observed 2026-08-06T18:50:52.687932Z

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Unavailable: canonical work link unavailable.

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Observation aa62f6cd-dfd3-4a4e-b945-c5c66a7a4227 · inbound

GASPnet: Global Agreement to Synchronize Phases cites this paper.

GASPnet: Global Agreement to Synchronize Phases On the Relationship between Self-Attention and Convolutional Layers

Reference 2019

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no resolver link, observed 2026-08-06T15:09:38.488716Z

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Unavailable: canonical work link unavailable.

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Observation e8243dd2-0513-4aee-9aa9-0748b6bfb7d6 · inbound

Long-Term Visual Localization in Dynamic Benthic Environments: A Dataset, Footprint-Based Ground Truth, and Visual Place Recognition Benchmark cites this paper.

Long-Term Visual Localization in Dynamic Benthic Environments: A Dataset, Footprint-Based Ground Truth, and Visual Place Recognition Benchmark On the Relationship between Self-Attention and Convolutional Layers

Reference 53

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no resolver link, observed 2026-08-02T19:02:06.386909Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T19:02:06.386909Z digest=sha256:ade5f33ec338e81a7f35f2da3499f9a324f55fd9b304b591dba6416eef1f7105

Observation 2f9ae193-2c21-4193-bc49-f806ac75c956 · inbound

From Sparsity to Simplicity: Enabling Simpler Sequential Replacements via Sparse Attention Distillation cites this paper.

From Sparsity to Simplicity: Enabling Simpler Sequential Replacements via Sparse Attention Distillation On the Relationship between Self-Attention and Convolutional Layers

Reference 3

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verified exact
arxiv_id, observed 2026-05-20T19:58:58.876272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 9602f92c-4912-4366-bcaf-45b692826d28 · inbound

Weierstrass Positional Encoding for Vision Transformers cites this paper.

Weierstrass Positional Encoding for Vision Transformers On the Relationship between Self-Attention and Convolutional Layers

Reference 12

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verified exact
arxiv_id, observed 2026-05-25T05:50:23.757240Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 16d9f984-092d-4ae0-b59a-fc8340e41136 · inbound

SWARD: Stochastic Window-Attention-Based Relational Distillation for Cross-Architectural Semantic Segmentation cites this paper.

SWARD: Stochastic Window-Attention-Based Relational Distillation for Cross-Architectural Semantic Segmentation On the Relationship between Self-Attention and Convolutional Layers

Reference 6

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verified exact
arxiv_id, observed 2026-07-01T20:56:13.946586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-28T17:37:46.185060Z digest=sha256:5a2bcaabaa328694b97ad2542b27412cf81ede774917960b4f361e8e121d70fa

Observation 8d83ee67-493e-4677-95ac-9c2df982570d · inbound

RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways cites this paper.

RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways On the Relationship between Self-Attention and Convolutional Layers

Reference 6

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verified exact
arxiv_id, observed 2026-07-03T04:37:36.441026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation f496aaba-5f4c-4d52-a914-586a87109bc6 · inbound

RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways cites this paper.

RoVE: Rotary Value Embeddings Attention for Relative Position-dependent Value Pathways On the Relationship between Self-Attention and Convolutional Layers

Reference 2019

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no resolver link, observed 2026-08-02T11:55:28.866330Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 420f9e7a-65d2-4324-a212-c901b3bcf3da · inbound

ITNet: A Learnable Integral Transform That Subsumes Convolution, Attention, and Recurrence cites this paper.

ITNet: A Learnable Integral Transform That Subsumes Convolution, Attention, and Recurrence On the Relationship between Self-Attention and Convolutional Layers

Reference 18

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verified exact
arxiv_id, observed 2026-07-04T00:59:20.817064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 2f79c0d3-8a15-4535-8401-004e2262af6f · inbound

ITNet: A Learnable Integral Transform That Subsumes Convolution, Attention, and Recurrence cites this paper.

ITNet: A Learnable Integral Transform That Subsumes Convolution, Attention, and Recurrence On the Relationship between Self-Attention and Convolutional Layers

Reference 18

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verified exact
arxiv_id, observed 2026-06-30T11:54:38.647194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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