Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2008.09202.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T05:39:11.753071Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
1
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 82fc12dc-f861-45af-97bc-e8d1285b4043 · inbound
Regression Augmentation With Data-Driven Segmentation cites this paper.
Regression Augmentation With Data-Driven Segmentation Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:39:11.753071Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ca96d674-ab87-43a7-bc84-6c99388d4f7d · inbound
Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning cites this paper.
Beyond Synthetic Augmentation: Group-Aware Threshold Calibration for Robust Balanced Accuracy in Imbalanced Learning Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T14:23:18.948443Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation daad78ef-a9fc-47ed-b76b-f750ce08f013 · inbound
Toward Calibrated, Fair, and accurate Deepfake Detection cites this paper.
Toward Calibrated, Fair, and accurate Deepfake Detection Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning
Reference 140
- Resolution
- verified exact
- arxiv_id, observed 2026-06-28T07:11:44.878300Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.