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Paper Citation Record · LEDGER

Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2010.03622.

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

pith.paper-citation-record.v1
2010.03622 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:24:48.988092Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T03:25:20.638040Z

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 09bada56-31f0-453b-92c7-ead74e709d1d · inbound

Lungmix: A Mixup-Based Strategy for Generalization in Respiratory Sound Classification cites this paper.

Lungmix: A Mixup-Based Strategy for Generalization in Respiratory Sound Classification Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-10T23:24:48.988092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:24:48.988092Z digest=sha256:c22f6875d4bfa64a07f0b7330d02f9dcc802a85ecaecdc05c08ad575349b0a97

Observation 2c63a570-d8b7-46dc-af19-8cd01504f32f · inbound

Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss cites this paper.

Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the Squared Loss Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-09T21:24:54.907492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T21:24:54.907492Z digest=sha256:2af5ed0cad49b1840166387ec0abb0fc11513e2b2c7372c737c4551cc46242cf

Observation ebb1ab20-6c39-4e8e-a5d0-236815ab7108 · inbound

Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension cites this paper.

Discrepancies are Virtue: Weak-to-Strong Generalization through Lens of Intrinsic Dimension Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-05-23T03:25:20.641779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-23T03:24:07.851782Z digest=sha256:821a93f6260a588cee9d4a7c7761bd915c9a1cf804f5e987ad58397c3d50305e

Observation e4c53829-dfec-4a06-a316-d6653c516f9a · 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 Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-07T15:32:16.278383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:32:16.278383Z digest=sha256:45db84c68f9912ebe10bcac5a5ba050e2196f498d47f367e7382b90ac834525b

Observation 96af1e9f-c27a-44b7-ac34-eacb472b4436 · inbound

Beyond Entropy: Region Confidence Proxy for Wild Test-Time Adaptation cites this paper.

Beyond Entropy: Region Confidence Proxy for Wild Test-Time Adaptation Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T13:56:15.537627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T13:56:15.537627Z digest=sha256:544967522f712a2bf8934d5d2692da5029d7581fec52eec2a70e46969d31e8c7

Observation 3cbad311-ee22-4eee-acda-30819629c3c8 · inbound

Direct Diffusion Score Preference Optimization via Stepwise Contrastive Policy-Pair Supervision cites this paper.

Direct Diffusion Score Preference Optimization via Stepwise Contrastive Policy-Pair Supervision Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-03T13:45:02.917554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T13:45:02.917554Z digest=sha256:a25539821208fbf8a6cce2fdfa470f7217c4f9dd7669a1c4d8e2578aeead4e85

Observation edfac61c-8dfc-4aa5-8326-283bae06f655 · inbound

Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs cites this paper.

Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

Reference 32

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T06:32:24.756624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-05-13T06:27:43.534185Z digest=sha256:6b91f3a459f050897f06d9a86fe72572edfef427623c787325eeae60f520c1b1

Observation 4d752c14-a225-404a-9d36-3a01aefb8ad8 · inbound

Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models cites this paper.

Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models Theoretical Analysis of Self-Training with Deep Networks on Unlabeled Data

Reference 65

Resolution
verified exact
arxiv_id, observed 2026-05-20T01:07:54.829763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=pdf_text observed=2026-05-20T01:03:22.678982Z digest=sha256:725d8caeac255f6b2840eb35337e3447d60ff3892f01b1715398ee0f4c75fe0a