Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
How Well Generative Adversarial Networks Learn Distributions
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1811.03179.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T10:57:15.347085Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T14:46:14.017693Z
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 9d194acb-e8c5-454a-a9d3-111653e0dbae · inbound
On the Minimax Optimality of Estimating the Wasserstein Metric cites this paper.
On the Minimax Optimality of Estimating the Wasserstein Metric How Well Generative Adversarial Networks Learn Distributions
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T10:57:15.347085Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 40b05757-43a4-4649-a738-d5c520f0d5a9 · inbound
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations cites this paper.
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations How Well Generative Adversarial Networks Learn Distributions
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:01:06.757982Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 54d0eb30-d78d-4b25-b205-50e1fcd6e6a9 · inbound
Causal Inference for Spatial Treatments cites this paper.
Causal Inference for Spatial Treatments How Well Generative Adversarial Networks Learn Distributions
Reference 100
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T14:46:14.020606Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.