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Paper Citation Record · LEDGER

ViP: A Differentially Private Foundation Model for Computer Vision

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.08842.

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

pith.paper-citation-record.v1
2306.08842 v2

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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T16:47:59.099339Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T00:02:30.392371Z

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 840ab18a-2696-4241-bfa9-f3460a51885a · inbound

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training cites this paper.

Leveraging Programmatically Generated Synthetic Data for Differentially Private Diffusion Training ViP: A Differentially Private Foundation Model for Computer Vision

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T16:47:59.099339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T16:47:59.099339Z digest=sha256:2d4823c51a2d82eec8ae8299692a8da3aef9c34c8f1615ff1dc6412af3630534

Observation d04f96f2-2e7d-43ea-a098-1dda0c2dc1c4 · inbound

PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization cites this paper.

PLRV-O: Advancing Differentially Private Deep Learning via Privacy Loss Random Variable Optimization ViP: A Differentially Private Foundation Model for Computer Vision

Reference 64

Resolution
verified exact
local_arxiv, observed 2026-08-05T00:02:30.398297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-05T00:02:30.330779Z digest=sha256:38444b506174e4b6970064f2d3d0ade976b36b6a9f4596d1a8c388f4009e0e14