Paper Citation Record · LEDGER

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies

As of 12 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2412.18296.

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

pith.paper-citation-record.v1
2412.18296 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T04:53:45.611536Z

measured 44 of 44 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:49:51.583731Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T13:49:52.943145Z

Reference resolution

43 of 43 outbound references displayed

verified exact0
verified fuzzy28
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 089e87ef-4dec-40a9-9bde-ae0d8ef7ccf6 · outbound

This paper cites K.; Stirling, W.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies K.; Stirling, W

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.567705Z

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.

Observation 382a415a-fd2c-4e92-ba7b-6ae4331710c3 · outbound

This paper cites M.; Nasrabadi, N.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies M.; Nasrabadi, N

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.549048Z

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.

Observation 59ef8893-6164-4e2e-b6fc-66e00d0f4aee · outbound

This paper cites Review for Handling Missing Data with special missing mechanism.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Review for Handling Missing Data with special missing mechanism

Reference 3

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Source-reported events for the cited work

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Observation f705086e-8738-4e72-8526-678a7b0f8172 · outbound

This paper cites A Survey on Missing Data in Machine Learning.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies A Survey on Missing Data in Machine Learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.529792Z

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.

Observation e2158ee8-7b78-450a-9eb6-8c6cfb691674 · outbound

This paper cites Compression of GNSS Data with the Aim of Speeding Up Communication to Autonomous Vehicles.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Compression of GNSS Data with the Aim of Speeding Up Communication to Autonomous Vehicles

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.511117Z

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.

Observation 59652a1c-9f0d-4598-a1e0-5333f5573f29 · outbound

This paper cites An Introduction to Probability Theory and Its Applications , 3rd ed., Vol.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies An Introduction to Probability Theory and Its Applications , 3rd ed., Vol

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.490020Z

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.

Observation 078679fa-c59c-4b74-aae5-51118a2b104b · outbound

This paper cites an unresolved cited work.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-11T04:53:46.469669Z

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.

Observation 0d8ba32e-8b78-4472-8f1d-6f95202d7cee · outbound

This paper cites an unresolved cited work.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Unresolved cited work

Reference 8

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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.

Observation 68979771-70df-4fce-8cd6-5cdf6c5ee72d · outbound

This paper cites L.; Graham, J.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies L.; Graham, J

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.429695Z

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.

Observation 4cfb99d2-2e50-46c2-8bdf-f58657232bc4 · outbound

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Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Unresolved cited work

Reference 10

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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.

Observation e75342e4-e6bc-4eb3-8d61-975d880e3830 · outbound

This paper cites an unresolved cited work.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Unresolved cited work

Reference 11

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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.

Observation 91e26a66-ecf5-44e2-887a-44a271a31eb9 · outbound

This paper cites an unresolved cited work.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Unresolved cited work

Reference 12

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Source-reported events for the cited work

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Observation 6df1c70b-c7e6-41ef-8b89-bb3eb9e75b28 · outbound

This paper cites P.; Laird, N.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies P.; Laird, N

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.349890Z

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.

Observation 5e80554a-c1de-4187-b2f7-2a1157f78ecc · outbound

This paper cites an unresolved cited work.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Unresolved cited work

Reference 14

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Source-reported events for the cited work

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Observation 9b367930-e7eb-4274-821a-f30fbe0dec62 · outbound

This paper cites H.; Olshen, R.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies H.; Olshen, R

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.301064Z

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.

Observation 4c61d200-1750-4c53-abf0-8fec113465cc · outbound

This paper cites Random Forests.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Random Forests

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.279987Z

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.

Observation 646d4d64-0f9f-48fb-8415-f745d845527b · outbound

This paper cites Extracting and Composing Robust Features with Denoising Autoencoders.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Extracting and Composing Robust Features with Denoising Autoencoders

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.257360Z

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.

Observation edd36407-2b03-40e3-a07c-af26bef8a6de · outbound

This paper cites Generative Adversarial Nets.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Generative Adversarial Nets

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.237084Z

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.

Observation 6b4f3bc9-25b4-47d6-9063-04a4621eef35 · outbound

This paper cites EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 19

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Source-reported events for the cited work

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Observation 4d8f69ec-dec2-4c6c-a349-ebea66b59b9e · outbound

This paper cites A Survey of Data Augmen- tation Approaches for NLP.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies A Survey of Data Augmen- tation Approaches for NLP

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.214724Z

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.

Observation 299d9244-0ed2-4a26-ae81-c920cff29a26 · outbound

This paper cites Missing Token Imputation Using Masked Language Models.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Missing Token Imputation Using Masked Language Models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.190157Z

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.

Observation 20f21d47-18c5-4d62-859f-277bc310ad0c · outbound

This paper cites Imputing Missing Sentences with Generative Adversarial Net- works.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Imputing Missing Sentences with Generative Adversarial Net- works

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.165891Z

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.

Observation 341c6539-d155-4844-8971-de7592743de5 · outbound

This paper cites Missing Data Imputation: Focusing on Single Imputation.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Missing Data Imputation: Focusing on Single Imputation

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.145553Z

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.

Observation eeef6b03-82ca-4b53-8498-6e6d66aa4188 · outbound

This paper cites Learning from Noisy Labels with Deep Neural Networks: A Survey.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Learning from Noisy Labels with Deep Neural Networks: A Survey

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.126228Z

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.

Observation ddcc4f98-7c2a-4491-b690-ec22c95700a4 · outbound

This paper cites Recurrent Neural Networks for Multivariate Time Series with Missing Values.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Recurrent Neural Networks for Multivariate Time Series with Missing Values

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.105728Z

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.

Observation 826a7777-f698-45e5-a073-86d62131976c · outbound

This paper cites GAIN: Missing Data Imputation Using Generative Adver- sarial Nets.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies GAIN: Missing Data Imputation Using Generative Adver- sarial Nets

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.085165Z

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.

Observation 93f22bbc-3730-4068-8b7b-25a95d96fad4 · outbound

This paper cites Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.064870Z

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.

Observation 01ed53ab-598a-46d9-a24b-28018b731b86 · outbound

This paper cites Deep Learning is Robust to Massive Label Noise.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Deep Learning is Robust to Massive Label Noise

Reference 28

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Source-reported events for the cited work

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Observation 52d52d9d-806a-47c4-9f59-ab174bc9b74d · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Explaining and Harnessing Adversarial Examples

Reference 29

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be03f7ef-aea9-4aaa-b7df-834cfa897d91 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 30

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Source-reported events for the cited work

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Observation 94215beb-c46c-4fa6-98df-f4de22807521 · outbound

This paper cites Language Models are Few-Shot Learners.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Language Models are Few-Shot Learners

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T04:53:45.542475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8f8f2ad-8b8e-47c2-8859-d4dbb13cf7bb · outbound

This paper cites M.; Gebru, T.; McMillan-Major, A.; Shmitchell, S.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies M.; Gebru, T.; McMillan-Major, A.; Shmitchell, S

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.044933Z

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.

Observation 99c3a751-a2f7-475a-a98f-ddbc4a252033 · outbound

This paper cites SimCSE: Simple Contrastive Learning of Sentence Embeddings.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies SimCSE: Simple Contrastive Learning of Sentence Embeddings

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T04:53:45.552988Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bb91bbf-2cdf-47fc-ac49-b8738c9e8675 · outbound

This paper cites S.; Zettlemoyer, L.; Levy, O.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies S.; Zettlemoyer, L.; Levy, O

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:46.024597Z

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.

Observation ddfa547d-4547-4759-85b8-85383a851d3f · outbound

This paper cites Deep Recurrent Q-learning for Partially Observable MDPs.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Deep Recurrent Q-learning for Partially Observable MDPs

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:45.998381Z

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.

Observation 6b2ce7a4-b198-4a1b-aede-649db24104c6 · outbound

This paper cites A Model-based Reinforcement Learning with Adversarial Training for Online Recommendation.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies A Model-based Reinforcement Learning with Adversarial Training for Online Recommendation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:45.975584Z

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.

Observation 70c3d6cc-f3a1-41b9-b7e9-2716665d3de5 · outbound

This paper cites Reinforcement Learning with Long Short-term Memory.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies Reinforcement Learning with Long Short-term Memory

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:45.954546Z

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.

Observation a2c6fd5f-32d6-486c-a39f-328956e91b71 · outbound

This paper cites A.; Veness, J.; Bellemare, M.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies A.; Veness, J.; Bellemare, M

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:45.932491Z

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.

Observation 9fd1ff9b-19cb-4ce9-8288-b3cf31a493a0 · outbound

This paper cites A.; Darrell, T.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies A.; Darrell, T

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:45.911711Z

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.

Observation 90457b5f-c06a-4441-9473-c4b3859aeea4 · outbound

This paper cites E.; Stone, P.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies E.; Stone, P

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:45.890570Z

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.

Observation 0282570d-2985-4baa-b40b-bd725b2d1287 · outbound

This paper cites KILT: a Benchmark for Knowledge Intensive Language Tasks.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies KILT: a Benchmark for Knowledge Intensive Language Tasks

Reference 41

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Source-reported events for the cited work

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Observation 00194d6a-8e63-415a-9594-73a1e7f632fd · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T04:53:45.603884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d7085c8-8195-4a73-972b-2f3ed5e3bec4 · outbound

This paper cites X.; Maharjan, S.

Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies X.; Maharjan, S

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T04:53:45.865176Z

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.

Pith citing papers

Observation a742f198-80a7-4984-b64e-2c570c48373e · inbound

Theory Foundation of Physics-Enhanced Residual Learning cites this paper.

Theory Foundation of Physics-Enhanced Residual Learning Navigating Data Corruption in Machine Learning: Balancing Quality, Quantity, and Imputation Strategies

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:49:53.011026Z

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.