Paper Citation Record · LEDGER

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information

As of 19 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2508.07713.

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

pith.paper-citation-record.v1
2508.07713 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:01:59.812979Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T22:57:18.895636Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T23:02:46.845989Z

Reference resolution

50 of 50 outbound references displayed

verified exact0
verified fuzzy40
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1f49a618-0201-4c1c-bde3-b55437ab6e8b · outbound

This paper cites write newline.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information write newline

Reference 1

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Observation 4b750ea4-cc7c-445d-a938-c18bcd12eace · outbound

This paper cites Unsupervised label noise modeling and loss correction.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Unsupervised label noise modeling and loss correction

Reference 2

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Observation 4467415a-9f51-48da-8aab-4a86ef02d956 · outbound

This paper cites A closer look at memorization in deep networks.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information A closer look at memorization in deep networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.512686Z

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Observation 59575dcc-fe4f-41ee-a1a3-df603812618e · outbound

This paper cites Beyond class-conditional assumption: A primary attempt to combat instance-dependent label noise.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Beyond class-conditional assumption: A primary attempt to combat instance-dependent label noise

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.488172Z

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Observation b96d61fd-cd86-4856-a7b1-747ae1323c0b · outbound

This paper cites BERT : P re-training of deep bidirectional transformers for language understanding.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information BERT : P re-training of deep bidirectional transformers for language understanding

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.466036Z

Source-reported events for the cited work

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Observation f36db7e2-716d-4c44-8dae-b24c7bc732de · outbound

This paper cites Classification in the presence of label noise: A survey.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Classification in the presence of label noise: A survey

Reference 6

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Source-reported events for the cited work

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Observation aa2d7082-88cf-4db0-b894-8bf93a8e0766 · outbound

This paper cites Robust loss functions under label noise for deep neural networks.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Robust loss functions under label noise for deep neural networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.365812Z

Source-reported events for the cited work

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Observation 5e5331c6-f9dc-4b34-a7d5-11ec54d8125d · outbound

This paper cites Co-teaching: R obust training of deep neural networks with extremely noisy labels.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Co-teaching: R obust training of deep neural networks with extremely noisy labels

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.338534Z

Source-reported events for the cited work

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Observation e14b8115-62bc-4f69-a7dc-df9d326d9bc2 · outbound

This paper cites Robot Data Curation with Mutual Information Estimators.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Robot Data Curation with Mutual Information Estimators

Reference 9

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Observation ce405490-b449-42f3-a9f2-4c07890b8861 · outbound

This paper cites Using trusted data to train deep networks on labels corrupted by severe noise.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Using trusted data to train deep networks on labels corrupted by severe noise

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.316907Z

Source-reported events for the cited work

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Observation f3af7b7b-07c4-489e-af3c-fb7bdcbea754 · outbound

This paper cites Using pre-training can improve model robustness and uncertainty.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Using pre-training can improve model robustness and uncertainty

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.292466Z

Source-reported events for the cited work

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Observation 65986243-4119-43e0-86dc-1eadb2f61e70 · outbound

This paper cites Universal language model fine-tuning for text classification.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Universal language model fine-tuning for text classification

Reference 12

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Source-reported events for the cited work

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Observation f9d03b6f-efe5-40f2-887c-3bd69b5d6ad4 · outbound

This paper cites Mentor N et: L earning data-driven curriculum for very deep neural networks on corrupted labels.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Mentor N et: L earning data-driven curriculum for very deep neural networks on corrupted labels

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.242169Z

Source-reported events for the cited work

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Observation c1f7f3e1-a802-4226-94ce-54fa0061cdd8 · outbound

This paper cites Understanding black-box predictions via influence functions.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Understanding black-box predictions via influence functions

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.221687Z

Source-reported events for the cited work

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Observation bc61aa6a-9ce8-435d-92a2-0e2cba8f9aee · outbound

This paper cites Submodular Mutual Information for Targeted Data Subset Selection.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Submodular Mutual Information for Targeted Data Subset Selection

Reference 15

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Observation d58f930e-4a2e-465f-b1ba-198d799c8674 · outbound

This paper cites Estimating mutual information.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Estimating mutual information

Reference 16

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Source-reported events for the cited work

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Observation 30716899-e6bd-45b3-82f6-fb0e3be14f8a · outbound

This paper cites Image N et classification with deep convolutional neural networks.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Image N et classification with deep convolutional neural networks

Reference 17

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Source-reported events for the cited work

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Observation 8ccf3fdd-bd04-433d-8769-1aad41813483 · outbound

This paper cites Clean N et: T ransfer learning for scalable image classifier training with label noise.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Clean N et: T ransfer learning for scalable image classifier training with label noise

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.153113Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b63f5583-8420-4fb0-ac63-5be6d7a7544f · outbound

This paper cites Divide M ix: L earning with noisy labels as semi-supervised learning.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Divide M ix: L earning with noisy labels as semi-supervised learning

Reference 19

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Observation 9acd904b-aa2d-450d-9b91-a25742de89b3 · outbound

This paper cites WebVision Database: Visual Learning and Understanding from Web Data.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information WebVision Database: Visual Learning and Understanding from Web Data

Reference 20

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Observation 157af35a-0d4c-4097-bf2f-02afc8d059b9 · outbound

This paper cites Early-learning regularization prevents memorization of noisy labels.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Early-learning regularization prevents memorization of noisy labels

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.114973Z

Source-reported events for the cited work

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Observation 590a95fd-3681-4d2c-b405-1f1ea44c07f2 · outbound

This paper cites Observer variation in the diagnosis of follicular variant of papillary thyroid carcinoma.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Observer variation in the diagnosis of follicular variant of papillary thyroid carcinoma

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.087616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f60020d2-2eff-4bae-978b-2640a66e3706 · outbound

This paper cites Does label smoothing mitigate label noise? In Proc.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Does label smoothing mitigate label noise? In Proc

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.067047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 41bd79c5-34f7-42ca-bcd2-7d726da371db · outbound

This paper cites Statistical Undersampling with Mutual Information and Support Points.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Statistical Undersampling with Mutual Information and Support Points

Reference 24

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Observation 53570c26-c49e-43a3-9265-f6eb5a92d052 · outbound

This paper cites Conducting behavioral research on amazon’s mechanical turk.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Conducting behavioral research on amazon’s mechanical turk

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.032909Z

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2b0f5103-ee33-4f35-b630-2f9fa6dee4e9 · outbound

This paper cites Neural information retrieval: A t the end of the early years.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Neural information retrieval: A t the end of the early years

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:02.003399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 067a8f6f-7e3e-4ee6-a4fc-c617b66109d3 · outbound

This paper cites Deeprank: A new deep architecture for relevance ranking in information retrieval.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Deeprank: A new deep architecture for relevance ranking in information retrieval

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.974716Z

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Observation 9c927ae1-24b3-487a-9c05-6f74fa10c71f · outbound

This paper cites Running experiments on amazon mechanical turk.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Running experiments on amazon mechanical turk

Reference 28

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Observation 13e5be61-8665-4a39-8ecf-34e6417f18b7 · outbound

This paper cites Making deep neural networks robust to label noise: A loss correction approach.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Making deep neural networks robust to label noise: A loss correction approach

Reference 29

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This paper cites You only look once: U nified, real-time object detection.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information You only look once: U nified, real-time object detection

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.901568Z

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Observation 0f7f3296-44e6-4cc3-a933-8e4bcbddf7d2 · outbound

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Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Twitter sentiment analysis with deep convolutional neural networks

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.866913Z

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Observation 50f64002-5f68-4035-a1d5-387067a79578 · outbound

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Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Unresolved cited work

Reference 32

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Observation c2d2aef0-e60d-4fb8-a299-0d4cb5f7f796 · outbound

This paper cites Meta- W eight- N et: L earning an explicit mapping for sample weighting.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Meta- W eight- N et: L earning an explicit mapping for sample weighting

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.825752Z

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Observation 895085be-b17f-45d3-80d1-053c477fa54e · outbound

This paper cites SELFIE : R efurbishing unclean samples for robust deep learning.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information SELFIE : R efurbishing unclean samples for robust deep learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.801581Z

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Observation 7394911e-cf5f-461e-a9ca-282abf05d55b · outbound

This paper cites Learning from noisy labels with deep neural networks: A survey.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Learning from noisy labels with deep neural networks: A survey

Reference 35

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This paper cites Interactive label cleaning with example-based explanations.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Interactive label cleaning with example-based explanations

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.758673Z

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Observation 54807e7b-cab4-445d-a4d5-cf8eefb8527f · outbound

This paper cites Symmetric cross entropy for robust learning with noisy labels.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Symmetric cross entropy for robust learning with noisy labels

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.739297Z

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Observation 2a270a45-606e-4f59-89d0-b21c7a140f16 · outbound

This paper cites To Smooth or Not? When Label Smoothing Meets Noisy Labels.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information To Smooth or Not? When Label Smoothing Meets Noisy Labels

Reference 38

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This paper cites Are anchor points really indispensable in label-noise learning? In Proc.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Are anchor points really indispensable in label-noise learning? In Proc

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.720515Z

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Observation 6f091dce-ea16-4727-9eb9-cef0be71e29f · outbound

This paper cites Robust early-learning: Hindering the memorization of noisy labels.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Robust early-learning: Hindering the memorization of noisy labels

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.698501Z

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Observation e613fac6-ff5c-44e5-b9d5-9b1014c36709 · outbound

This paper cites Adversarial label flips attack on support vector machines.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Adversarial label flips attack on support vector machines

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.673439Z

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Observation dde887c4-9222-45af-82e6-92ec7f5559e7 · outbound

This paper cites Learning from massive noisy labeled data for image classification.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Learning from massive noisy labeled data for image classification

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.653979Z

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Observation d644869c-5761-4248-a17b-3c9f0c044b98 · outbound

This paper cites Relabeling Minimal Training Subset to Flip a Prediction.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Relabeling Minimal Training Subset to Flip a Prediction

Reference 43

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Observation 732edc33-8ce6-4e91-98bf-4ca9c52d21e1 · outbound

This paper cites Dual T : Reducing estimation error for transition matrix in label-noise learning.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Dual T : Reducing estimation error for transition matrix in label-noise learning

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.505732Z

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Observation a0d9c723-9d67-4e62-b981-37d489f0e954 · outbound

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Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Mutual information based data selection in gaussian processes for people tracking

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:01.215800Z

Source-reported events for the cited work

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Observation 466043e1-ab49-4ee9-a6ab-96dec8655aae · outbound

This paper cites Understanding deep learning requires rethinking generalization.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Understanding deep learning requires rethinking generalization

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:00.975450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2bfa0853-dbdb-49e4-8273-5ab611df2ae0 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information mixup: Beyond Empirical Risk Minimization

Reference 47

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This paper cites Deep learning over multi-field categorical data.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Deep learning over multi-field categorical data

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:00.712085Z

Source-reported events for the cited work

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Observation d69e9dac-193f-42e1-b3b3-3cbfac989a48 · outbound

This paper cites Generalized cross entropy loss for training deep neural networks with noisy labels.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Generalized cross entropy loss for training deep neural networks with noisy labels

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:00.455376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e942fc1c-ca52-43f0-acc8-cfd5a0e9d773 · outbound

This paper cites Class noise vs.

Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information Class noise vs

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:02:00.194856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 6f39a794-ad9c-4df5-a1c0-6bc2fff122d7 · inbound

InfoAtlas: A Foundation Model for Zero-Shot Statistical Dependence Estimate cites this paper.

InfoAtlas: A Foundation Model for Zero-Shot Statistical Dependence Estimate Detecting Mislabeled and Corrupted Data via Pointwise Mutual Information

Reference 236

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:02:46.847324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.