Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards Measuring Membership Privacy
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1712.09136.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T18:12:46.771953Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-04T12:39:50.039430Z
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 34e2c038-8672-4aaf-80b7-e3af579b4694 · inbound
Rethinking Membership Inference Attacks Against Transfer Learning cites this paper.
Rethinking Membership Inference Attacks Against Transfer Learning Towards Measuring Membership Privacy
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T18:12:46.771953Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d1ad026e-c021-4e8f-a5c5-5c23720162f5 · inbound
Membership Inference Attacks Against Vision-Language Models cites this paper.
Membership Inference Attacks Against Vision-Language Models Towards Measuring Membership Privacy
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:01:10.985169Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fb8b895f-19b3-4ed5-9689-4144383c8788 · inbound
Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions cites this paper.
Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions Towards Measuring Membership Privacy
Reference 230
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-04T12:39:50.040587Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.