Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2301.09696.
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 2026-08-09T12:44:17.793993Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T05:55:57.685030Z
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 51552647-db70-4ad0-bf9f-58b0576f6697 · inbound
Density Ratio Estimation with Conditional Probability Paths cites this paper.
Density Ratio Estimation with Conditional Probability Paths Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T12:44:17.793993Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9864059e-65ac-4e04-90ae-beb74698e96d · inbound
A unifying view of contrastive learning, importance sampling, and bridge sampling for energy-based models cites this paper.
A unifying view of contrastive learning, importance sampling, and bridge sampling for energy-based models Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T05:55:57.688785Z
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.