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Paper Citation Record · LEDGER

ProMix: Combating Label Noise via Maximizing Clean Sample Utility

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2207.10276.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2207.10276 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:43:35.422423Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-21T05:23:58.320849Z

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 64b04bde-19f4-4e83-b4ab-35aa6272fb64 · inbound

Open set label noise learning with robust sample selection and margin-guided module cites this paper.

Open set label noise learning with robust sample selection and margin-guided module ProMix: Combating Label Noise via Maximizing Clean Sample Utility

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-10T21:43:35.422423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T21:43:35.422423Z digest=sha256:5a7e4ff184e528cc7463d817ce3bab9f96ed13e97930159de79acb6f25712c0b

Observation 85bc6b5e-5767-485c-a4b5-4186da3c1eca · inbound

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling cites this paper.

Predicting Formula 1 Race Outcomes: Decomposing the Roles of Drivers and Constructors through Linear Modeling ProMix: Combating Label Noise via Maximizing Clean Sample Utility

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T10:20:10.593164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:20:10.593164Z digest=sha256:257275bc75dedcc102eadbe56e94b1b0a519c666ea456a1adc6017e080c1105f

Observation 8445c501-c2ea-4122-ac4b-9d506c00ab8e · inbound

HamBR: Active Decision Boundary Restoration Based on Hamiltonian Dynamics for Learning with Noisy Labels cites this paper.

HamBR: Active Decision Boundary Restoration Based on Hamiltonian Dynamics for Learning with Noisy Labels ProMix: Combating Label Noise via Maximizing Clean Sample Utility

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-13T02:17:06.685275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-13T02:13:21.564887Z digest=sha256:44be4db68319d9ccb243b78de7e0889345653155f96cb7e99b0637729cb4a472

Observation 5ddad225-35a8-44be-b10b-01acc0af07ec · inbound

Holistic Reliability Propagation: Decoupling Annotation and Prediction for Robust Noisy-Label cites this paper.

Holistic Reliability Propagation: Decoupling Annotation and Prediction for Robust Noisy-Label ProMix: Combating Label Noise via Maximizing Clean Sample Utility

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-21T05:23:58.323113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-21T05:21:33.160342Z digest=sha256:8c1f710fcdf8a7cbcd9ccf433dd1596075c26b49ac003275515bf5d1f0e78e7e

Observation 7c8eb1fd-7c53-43f4-ae88-b50675371af2 · inbound

GAMR: Geometric-Aware Manifold Regularization with Virtual Outlier Synthesis for Learning with Noisy Labels cites this paper.

GAMR: Geometric-Aware Manifold Regularization with Virtual Outlier Synthesis for Learning with Noisy Labels ProMix: Combating Label Noise via Maximizing Clean Sample Utility

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-05-21T05:19:39.222713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-21T05:17:14.304001Z digest=sha256:8dee2dbbb8c9cc5905d82b04d119873ced533575f6d23311f795aae8b7d7ceb1