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Paper Citation Record · LEDGER

Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1909.02210.

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

pith.paper-citation-record.v1
1909.02210 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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T00:05:14.914080Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T06:26:23.318288Z

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 6a34e34f-b3cf-4d31-b6f6-405f738a7c99 · inbound

The Statistical Cost of Adaptation in Multi-Source Transfer Learning cites this paper.

The Statistical Cost of Adaptation in Multi-Source Transfer Learning Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations

Reference 226

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:26:23.320929Z

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=arxiv_source observed=2026-05-12T04:19:05.837824Z digest=sha256:ef1b24ae94638256ae3fb44d98b84eb74e411f86da3fb1f00e7088c5483b94e5

Observation d00c5fec-4508-4a15-b877-78a5faa5e16b · inbound

Towards Optimal Estimators for Randomized Control Trials cites this paper.

Towards Optimal Estimators for Randomized Control Trials Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-01T00:05:14.914080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T00:05:14.914080Z digest=sha256:6f2fc5ad6e6837caf30421a46ae5904c42164ae969acc2f494ab02e439848ce1