Paper Citation Record · LEDGER

Deep Learning is Robust to Massive Label Noise

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 21 inbound Pith citation observations for arXiv:1705.10694.

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

pith.paper-citation-record.v1
1705.10694 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 21 of 21 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:42:38.099351Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

410
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 65ba76ed-aab8-4005-b661-1df29d995d52 · inbound

Detecting Spiky Corruption in Markov Decision Processes cites this paper.

Detecting Spiky Corruption in Markov Decision Processes Deep Learning is Robust to Massive Label Noise

Reference 15

Resolution: metadata mismatch; local_arxiv, observed 2026-05-25T12:20:47.704546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 701c0ff5-d87f-4937-98ec-489f55c6b3db · inbound

Supervised Classifiers for Audio Impairments with Noisy Labels cites this paper.

Supervised Classifiers for Audio Impairments with Noisy Labels Deep Learning is Robust to Massive Label Noise

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-25T10:10:37.650854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 89d3b974-0082-4219-afed-b67be7dc6262 · inbound

Product Image Recognition with Guidance Learning and Noisy Supervision cites this paper.

Product Image Recognition with Guidance Learning and Noisy Supervision Deep Learning is Robust to Massive Label Noise

Reference 28

Resolution: verified exact; local_arxiv, observed 2026-05-24T16:09:39.760845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a4a37d72-f5f1-4814-904f-66a181922e68 · inbound

Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels cites this paper.

Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels Deep Learning is Robust to Massive Label Noise

Reference 24

Resolution: metadata mismatch; local_arxiv, observed 2026-05-23T08:32:44.121899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7cc41328-364c-4648-8630-c56dce1143b0 · inbound

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model cites this paper.

SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model Deep Learning is Robust to Massive Label Noise

Reference 217

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:30:03.016569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e70c6b57-36fa-4b3a-8c0f-fcadd44d65b0 · inbound

Reinforcement Learning for Reasoning in Large Language Models with One Training Example cites this paper.

Reinforcement Learning for Reasoning in Large Language Models with One Training Example Deep Learning is Robust to Massive Label Noise

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-05-15T19:51:05.074776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 02615e3b-97d9-4db8-a69e-c0b64b66bbf0 · inbound

Real-Time Black-Box Optimization for Dynamic Discrete Environments Using Embedded Ising Machines cites this paper.

Real-Time Black-Box Optimization for Dynamic Discrete Environments Using Embedded Ising Machines Deep Learning is Robust to Massive Label Noise

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T23:42:38.099351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df0102a5-2c21-492f-9eae-4ddf6277fcf7 · inbound

GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training cites this paper.

GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training Deep Learning is Robust to Massive Label Noise

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-15T13:49:27.721549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61fdad84-73cc-467c-b269-a5b90128ae01 · inbound

Truth as a Compression Artifact in Language Model Training cites this paper.

Truth as a Compression Artifact in Language Model Training Deep Learning is Robust to Massive Label Noise

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-15T12:25:35.648665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 346d5147-aa68-4bba-b59c-7492f28f0abf · inbound

FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning cites this paper.

FB-NLL: A Feature-Based Approach to Tackle Noisy Labels in Personalized Federated Learning Deep Learning is Robust to Massive Label Noise

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-10T02:53:29.305005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9d0ea3ce-5874-4ac0-b27a-7e2b215f52ec · inbound

BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature cites this paper.

BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature Deep Learning is Robust to Massive Label Noise

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:36:04.685811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d97cbf83-c071-4e7c-9eca-5b0ca8eec73e · inbound

Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants cites this paper.

Inferring Asteroseismic Parameters from Short Observations Using Deep Learning: Application to TESS and K2 Red Giants Deep Learning is Robust to Massive Label Noise

Reference 68

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T02:20:54.070751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a5045a2e-2e1b-43ad-91d8-8a1fb7d26a2d · inbound

Multi-Block Attention for Efficient Channel Estimation in IRS-Assisted mmWave MIMO cites this paper.

Multi-Block Attention for Efficient Channel Estimation in IRS-Assisted mmWave MIMO Deep Learning is Robust to Massive Label Noise

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-05-15T03:19:44.245718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 7417f3d0-22cc-4f69-b523-4c71ae92c0c1 · inbound

Robust Fuzzy Multi-view Learning under View Conflict cites this paper.

Robust Fuzzy Multi-view Learning under View Conflict Deep Learning is Robust to Massive Label Noise

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-06-30T14:24:45.290491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2c54d0b5-5fda-43b0-83ae-e6b32b2e7ed2 · inbound

Learning from almost nothing: How neural networks survive heavy input corruption cites this paper.

Learning from almost nothing: How neural networks survive heavy input corruption Deep Learning is Robust to Massive Label Noise

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-07-03T04:17:36.687084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e53d9d20-5870-409e-a292-c60acb41750b · inbound

Noise-Aware Framework for Correcting Corrupted Labels cites this paper.

Noise-Aware Framework for Correcting Corrupted Labels Deep Learning is Robust to Massive Label Noise

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-07-03T09:07:47.578012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6d32c2d0-d8b6-4bcb-94d9-22da4912daf6 · inbound

Landsat-Sentinel-2 Algal Bloom Mapping Using Vision Transformers: Model Description, Implementation, and Examples cites this paper.

Landsat-Sentinel-2 Algal Bloom Mapping Using Vision Transformers: Model Description, Implementation, and Examples Deep Learning is Robust to Massive Label Noise

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T17:38:44.326451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f8f36d13-1cd7-4a0e-9cf0-4b98372564cd · inbound

Scaling Dense Retrieval with LLM-Annotated Training Data: Structured Mining and Progressive Curriculum for E-Commerce Sponsored Search cites this paper.

Scaling Dense Retrieval with LLM-Annotated Training Data: Structured Mining and Progressive Curriculum for E-Commerce Sponsored Search Deep Learning is Robust to Massive Label Noise

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-07-04T12:39:50.061878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8cfdf18d-a6d4-41ed-a2f3-0b2ce3c4477d · inbound

Uncertainty-aware tree height change regression cites this paper.

Uncertainty-aware tree height change regression Deep Learning is Robust to Massive Label Noise

Reference 45

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T14:37:03.044258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e055a0c8-4383-44e2-9fd7-32c0a06034d3 · inbound

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) cites this paper.

A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA) Deep Learning is Robust to Massive Label Noise

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-14T06:14:56.071808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 114e80ec-929d-4fed-a94a-67ab4e386bdb · inbound

Rater State Bias in RLHF Preference Data: An Audit Framework cites this paper.

Rater State Bias in RLHF Preference Data: An Audit Framework Deep Learning is Robust to Massive Label Noise

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-02T16:22:43.918789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.