Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
An Online Learning Approach to Generative Adversarial Networks
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1706.03269.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-14T14:03:37.540627Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T15:31:21.597824Z
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 4cd6efc9-05b2-4b07-b16a-2e2ea47cb98c · inbound
CMB-GAN: Fast Simulations of Cosmic Microwave background anisotropy maps using Deep Learning cites this paper.
CMB-GAN: Fast Simulations of Cosmic Microwave background anisotropy maps using Deep Learning An Online Learning Approach to Generative Adversarial Networks
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T14:03:37.540627Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d78b7010-2d0f-4273-af59-833c493cf3b5 · inbound
Nested Annealed Training Scheme for Generative Adversarial Networks cites this paper.
Nested Annealed Training Scheme for Generative Adversarial Networks An Online Learning Approach to Generative Adversarial Networks
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T18:30:58.518025Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 386e124d-49fa-4d21-abcf-66543b4f191b · inbound
A unified perspective on fine-tuning and sampling with diffusion and flow models cites this paper.
A unified perspective on fine-tuning and sampling with diffusion and flow models An Online Learning Approach to Generative Adversarial Networks
Reference 88
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T21:59:56.622867Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.