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Paper Citation Record · LEDGER

Prescribed Generative Adversarial Networks

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1910.04302.

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

pith.paper-citation-record.v1
1910.04302 v1

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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:54:50.621819Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T10:45:24.987444Z

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 ea37a12b-be95-45ef-b602-e714b715632c · inbound

Measuring Diversity in Synthetic Datasets cites this paper.

Measuring Diversity in Synthetic Datasets Prescribed Generative Adversarial Networks

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-08T04:54:50.621819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T04:54:50.621819Z digest=sha256:6adbf3497b557287816f3a27d5361a2e1456b6cb59313d9b51d380cdb79a7a93

Observation 30d86d52-a1ff-4ec2-b99d-1b8d939309ab · inbound

Exploring bidirectional bounds for minimax-training of Energy-based models cites this paper.

Exploring bidirectional bounds for minimax-training of Energy-based models Prescribed Generative Adversarial Networks

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-07T10:45:24.991034Z

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=2026-08-07T10:45:24.646286Z digest=sha256:bc5f600f7636b797daaafbe26909af6656f8c3670cdecdce663d03111df64a8d