Paper Citation Record · LEDGER

Escaping the Big Data Paradigm with Compact Transformers

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 23 inbound Pith citation observations for arXiv:2104.05704.

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

pith.paper-citation-record.v1
2104.05704 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 23 of 23 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 23 of 23 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:41:01.298875Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

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External citation measurements

296
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 717870e9-5ab3-4364-a5f6-c01970772fe0 · inbound

Maximizing the Position Embedding for Vision Transformers with Global Average Pooling cites this paper.

Maximizing the Position Embedding for Vision Transformers with Global Average Pooling Escaping the Big Data Paradigm with Compact Transformers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T10:41:01.298875Z

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Observation 00a3365f-f071-4399-bca7-417cd8831cd3 · inbound

A Retrospective Systematic Study on Hierarchical Sparse Query Transformer-assisted Ultrasound Screening for Early Hepatocellular Carcinoma cites this paper.

A Retrospective Systematic Study on Hierarchical Sparse Query Transformer-assisted Ultrasound Screening for Early Hepatocellular Carcinoma Escaping the Big Data Paradigm with Compact Transformers

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T00:50:40.309836Z

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Observation a0ff1e1a-6afc-4e0c-bcd6-1b34cf96b4d8 · inbound

Compress image to patches for Vision Transformer cites this paper.

Compress image to patches for Vision Transformer Escaping the Big Data Paradigm with Compact Transformers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T19:24:18.724635Z

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Observation 56a98dca-949b-443b-ae36-a3823162cbca · inbound

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

IAFormer: Interaction-Aware Transformer network for collider data analysis Escaping the Big Data Paradigm with Compact Transformers

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-22T17:14:59.473941Z

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Observation 98c62ac3-a01d-44a5-9e82-6044d732fc29 · inbound

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

Low-latency vision transformers via large-scale multi-head attention Escaping the Big Data Paradigm with Compact Transformers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T21:39:34.594967Z

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Observation c0458d5c-8b87-46d3-ad6c-e223a74e59e8 · inbound

DFYP: A Dynamic Fusion Framework with Spectral Channel Attention and Adaptive Operator learning for Crop Yield Prediction cites this paper.

DFYP: A Dynamic Fusion Framework with Spectral Channel Attention and Adaptive Operator learning for Crop Yield Prediction Escaping the Big Data Paradigm with Compact Transformers

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:27.558346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 023248b6-a09b-4937-8faa-2666a100dca4 · inbound

Comparative Analysis of Vision Transformers and Traditional Deep Learning Approaches for Automated Pneumonia Detection in Chest X-Rays cites this paper.

Comparative Analysis of Vision Transformers and Traditional Deep Learning Approaches for Automated Pneumonia Detection in Chest X-Rays Escaping the Big Data Paradigm with Compact Transformers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T18:16:22.369461Z

Source-reported events for the cited work

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Observation 483d0c2d-0c24-4ec2-9fa5-6340f32ba8d4 · inbound

A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs cites this paper.

A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs Escaping the Big Data Paradigm with Compact Transformers

Reference 143

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:06.930188Z

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Observation 11420835-4109-4311-a7d5-30218ca51e7c · inbound

A Survey on Generative Model Unlearning: Fundamentals, Taxonomy, Evaluation, and Future Direction cites this paper.

A Survey on Generative Model Unlearning: Fundamentals, Taxonomy, Evaluation, and Future Direction Escaping the Big Data Paradigm with Compact Transformers

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-06T13:54:39.687861Z

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Observation ef8e7f9b-7ebc-4014-a6d5-e324f47afdb4 · inbound

Enhancing compact convolutional transformers with super attention cites this paper.

Enhancing compact convolutional transformers with super attention Escaping the Big Data Paradigm with Compact Transformers

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T16:06:14.398738Z

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Observation 6fabdab6-57d1-4bee-b322-4008fc0d6c37 · inbound

Learning Mechanism Underlying NLP Pre-Training and Fine-Tuning cites this paper.

Learning Mechanism Underlying NLP Pre-Training and Fine-Tuning Escaping the Big Data Paradigm with Compact Transformers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T11:01:20.370715Z

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Observation 4e90bf99-6f83-484e-8bcd-9a6f7d40f551 · inbound

Rethinking the long-range dependency in Mamba/SSM and transformer models cites this paper.

Rethinking the long-range dependency in Mamba/SSM and transformer models Escaping the Big Data Paradigm with Compact Transformers

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-05T10:22:53.221304Z

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Observation 79c06a00-a408-4693-b426-9609376b8b7d · inbound

CoMViT: An Efficient Vision Backbone for Supervised Classification in Medical Imaging cites this paper.

CoMViT: An Efficient Vision Backbone for Supervised Classification in Medical Imaging Escaping the Big Data Paradigm with Compact Transformers

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T07:01:11.633507Z

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Observation 75eb2dcf-bf6b-455b-94fa-19f26966559a · inbound

CoMViT: An Efficient Vision Backbone for Supervised Classification in Medical Imaging cites this paper.

CoMViT: An Efficient Vision Backbone for Supervised Classification in Medical Imaging Escaping the Big Data Paradigm with Compact Transformers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T07:01:11.756939Z

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Observation 5fa1f4ff-efba-45d1-bd39-057fdcb5f286 · inbound

Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence cites this paper.

Pushing the limits of one-dimensional NMR spectroscopy for automated structure elucidation using artificial intelligence Escaping the Big Data Paradigm with Compact Transformers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T15:01:50.533239Z

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Observation 31de13c9-51f2-4adc-908f-b01c115e2756 · inbound

Street-Legal Physical-World Adversarial Rim for License Plates cites this paper.

Street-Legal Physical-World Adversarial Rim for License Plates Escaping the Big Data Paradigm with Compact Transformers

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T21:23:17.232217Z

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.

Observation 3a06584f-cd40-4187-ad89-f23d961dd6f6 · inbound

Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget cites this paper.

Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Escaping the Big Data Paradigm with Compact Transformers

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:56:06.646091Z

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.

Observation ef33ba2e-dc5d-493a-8333-715367935597 · inbound

Are Candidate Models Really Needed for Active Learning? cites this paper.

Are Candidate Models Really Needed for Active Learning? Escaping the Big Data Paradigm with Compact Transformers

Reference 148

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T05:19:45.906204Z

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.

Observation 101340c9-4634-40ca-8b74-7a89db36d1a5 · inbound

FTerViT: Fully Ternary Vision Transformer cites this paper.

FTerViT: Fully Ternary Vision Transformer Escaping the Big Data Paradigm with Compact Transformers

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T06:03:59.479046Z

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.

Observation b15b511b-786e-4a9e-8c66-5b1c2f2b936c · inbound

Building The Ph(ysical)AI Layer Of Machine Intelligence cites this paper.

Building The Ph(ysical)AI Layer Of Machine Intelligence Escaping the Big Data Paradigm with Compact Transformers

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:46:29.146289Z

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

Observation d2356dfa-142a-4f63-9ce1-3ff2112fbbc3 · inbound

AEGIS: A Multi-Task Joint-Embedding Predictive Architecture for Mammography cites this paper.

AEGIS: A Multi-Task Joint-Embedding Predictive Architecture for Mammography Escaping the Big Data Paradigm with Compact Transformers

Reference 17

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T19:07:17.281678Z

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.

Observation 0be5c44d-127c-4cba-be13-54d86c005763 · inbound

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating cites this paper.

AutoNorm: Understanding Adaptive Normalization in Transformers through Differentiable Gating Escaping the Big Data Paradigm with Compact Transformers

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-14T10:36:03.467256Z

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Observation ccc7e7c6-45ba-4aa5-a70e-ed2d72c89aef · inbound

Sharpness-Aware Minimization and Muon: Robustness under the Spectral Norm cites this paper.

Sharpness-Aware Minimization and Muon: Robustness under the Spectral Norm Escaping the Big Data Paradigm with Compact Transformers

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T00:57:39.004226Z

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Unavailable: canonical work link unavailable.