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Paper Citation Record · LEDGER

SELF: Learning to Filter Noisy Labels with Self-Ensembling

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1910.01842.

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

pith.paper-citation-record.v1
1910.01842 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T22:50:53.683602Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T23:58:42.740540Z

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 0f20ca6a-7a24-427a-ac92-eed0d02bfb06 · inbound

Unleashing the Power of Large Language Model for Denoising Recommendation cites this paper.

Unleashing the Power of Large Language Model for Denoising Recommendation SELF: Learning to Filter Noisy Labels with Self-Ensembling

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T22:50:53.683602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T22:50:53.683602Z digest=sha256:3674c14467d03e1133d62491f25999fb6416b0b5d2479c8fb7edfce29a29f127

Observation f8eabbe0-fb06-44e3-b0dc-c6c25b605aa0 · inbound

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing cites this paper.

Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing SELF: Learning to Filter Noisy Labels with Self-Ensembling

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-07T15:32:15.265408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:32:15.265408Z digest=sha256:d3460453f2027ca9d2aae7e9869782f2957c93e671678024f0ee1b0baf993d98

Observation 6aa543a3-6115-49ba-a754-bcb08d14409a · inbound

Why Can Accurate Models Be Learned from Inaccurate Annotations? cites this paper.

Why Can Accurate Models Be Learned from Inaccurate Annotations? SELF: Learning to Filter Noisy Labels with Self-Ensembling

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-07T15:10:12.600687Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:10:12.600687Z digest=sha256:18d3226752e71b36d0755f7ffead020baf5021d51bf872f203e34ef9be196244

Observation 98226178-f687-4e90-889f-9e00a7ee588c · inbound

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images cites this paper.

MoMBS: Mixed-order minibatch sampling enhances model training from diverse-quality images SELF: Learning to Filter Noisy Labels with Self-Ensembling

Reference 116

Resolution
unresolved
no resolver link, observed 2026-08-07T14:31:20.435376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:31:20.435376Z digest=sha256:c4c06222578cd942101c0261f9289eda08e8c75072f4a6690524fa2691017f4a

Observation f01e8846-4b27-40b8-8f23-1efdd74b023d · 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 SELF: Learning to Filter Noisy Labels with Self-Ensembling

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-07T14:14:22.275291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:14:22.275291Z digest=sha256:47a1193c1b71a9b5a62e3520c6691b88b1e6ef630ee869076e6c81cae3b87b62

Observation aca5d7a5-c54a-41dd-a2e8-69059efadc4b · inbound

Commuting Distance Regularization for Timescale-Dependent Label Inconsistency in EEG Emotion Recognition cites this paper.

Commuting Distance Regularization for Timescale-Dependent Label Inconsistency in EEG Emotion Recognition SELF: Learning to Filter Noisy Labels with Self-Ensembling

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-06T17:28:41.422945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:28:41.422945Z digest=sha256:797d268830908b9901e904f4578006207e105f5e514e20630dc0ce4996ee8b8d

Observation b82dc2f0-d55c-4bc7-9cd0-e909241983b3 · inbound

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition cites this paper.

Tackling the Noisy Elephant in the Room: Label Noise-robust Out-of-Distribution Detection via Loss Correction and Low-rank Decomposition SELF: Learning to Filter Noisy Labels with Self-Ensembling

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-04T22:55:05.220582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:55:05.220582Z digest=sha256:17217428df60cc07df556ec3163540f6c236b012b8859eac1d73de199eb28ecf

Observation 28e9f8f4-9574-42e8-982d-1656dca018b8 · inbound

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook cites this paper.

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook SELF: Learning to Filter Noisy Labels with Self-Ensembling

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-16T23:58:42.742274Z

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.

source=pdf_text observed=2026-05-16T23:57:11.100992Z digest=sha256:9e0584ef3bc59be2b9e581bb6c40abbb9030abd8e69351164a922c9f6fe80900

Observation 82c96488-079e-4cdb-8e03-07eacb4addfc · inbound

Can LLMs Learn to Reason Robustly under Noisy Supervision? cites this paper.

Can LLMs Learn to Reason Robustly under Noisy Supervision? SELF: Learning to Filter Noisy Labels with Self-Ensembling

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-13T17:08:01.338408Z

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.

source=pdf_text observed=2026-05-13T16:58:42.129870Z digest=sha256:828edea4867ca95d65942842dac9f3b8f9b6bf67ce847ec610d9f70e2bb1e0fe