Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1804.06872.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T14:14:21.050821Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-04T05:39:40.135400Z
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 369ae808-99cc-4f27-be75-dc5ae437283e · inbound
Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement cites this paper.
Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T14:14:21.050821Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fc99dc0c-aa0d-435f-b1f3-61e31d679126 · inbound
Model-agnostic information transfer and fusion for classification with label noise cites this paper.
Model-agnostic information transfer and fusion for classification with label noise Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T00:21:22.758447Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 8d14b61e-cb0f-4195-b4ca-ebf24a0a4af4 · inbound
AURA: Adaptive Uncertainty-aware Refinement for LLM-as-a-Judge Auditing cites this paper.
AURA: Adaptive Uncertainty-aware Refinement for LLM-as-a-Judge Auditing Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Reference 24
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-04T05:39:40.137586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.