Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2212.09321.
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 2026-08-12T11:56:54.387972Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T15:02:41.270428Z
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 aac98422-4b95-48d0-9b38-34f0fa6dd642 · inbound
Selfish Evolution: Making Discoveries in Extreme Label Noise with the Help of Overfitting Dynamics cites this paper.
Selfish Evolution: Making Discoveries in Extreme Label Noise with the Help of Overfitting Dynamics Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T11:56:54.387972Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a7c1bf7-7f09-4d15-9bea-10d71d7fac41 · inbound
LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training cites this paper.
LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training Learning from Training Dynamics: Identifying Mislabeled Data Beyond Manually Designed Features
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T15:02:41.273443Z
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.