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Paper Citation Record · LEDGER

A Theoretical Analysis of Self-Supervised Learning for Vision Transformers

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.02233.

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

pith.paper-citation-record.v1
2403.02233 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T20:53:40.258611Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T08:56:02.615014Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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 9b8c1007-ece6-4e5f-bd28-1ffd1921598a · inbound

Reverse Convolution and Its Applications to Image Restoration cites this paper.

Reverse Convolution and Its Applications to Image Restoration A Theoretical Analysis of Self-Supervised Learning for Vision Transformers

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-05T20:53:40.258611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:53:40.258611Z digest=sha256:7aedba76cc5c1703842ae546a77f5229e2af91c92fe5e475435547f807d76cb9

Observation bb2c4937-1c70-4634-88f2-61ed35f35268 · inbound

Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMs cites this paper.

Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMs A Theoretical Analysis of Self-Supervised Learning for Vision Transformers

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-11T08:56:02.627288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-10T16:23:50.751356Z digest=sha256:8627881f530a8f116be5e9893c71e9511a0d60686713aea9f21fb521d8adc22f