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Paper Citation Record · LEDGER

Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1910.14137.

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

pith.paper-citation-record.v1
1910.14137 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 2026-08-08T15:50:58.628072Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T23:26:55.179637Z

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 c2620c6d-b7ff-4b85-b771-c69c1e416776 · inbound

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead cites this paper.

Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-08T15:50:58.628072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T15:50:58.628072Z digest=sha256:cfa7d657551c9f757b0fb79bd68a23eb97c6b99668797b1e9929b3ab599127be

Observation fd4e3fac-4767-445e-94e3-f751e6f007d4 · inbound

Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification cites this paper.

Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-08-06T23:26:55.185147Z

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=2026-08-06T23:26:44.540330Z digest=sha256:d305ec760d2a41a7993dfac9808f067e1c7dafb148eae465bd0b40947a15cf97