Paper Citation Record · LEDGER

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling

As of 23 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2608.04147.

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

pith.paper-citation-record.v1
2608.04147 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:48:47.448433Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy15
unresolved14
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 456164f2-0018-443f-a4e6-11d95f1d7dd3 · outbound

This paper cites Estimating example difficulty using vari- ance of gradients, 2022.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Estimating example difficulty using vari- ance of gradients, 2022

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:48.465510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f3c3b51f-f6a0-4176-8a2d-d25a36914112 · outbound

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

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling A closer look at memorization in deep networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:48.449697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 24a2a35b-6352-4e1e-b710-8ffeadd797ae · outbound

This paper cites Mixmatch: A holistic approach to semi-supervised learning.Advances in neural information processing systems, 32, 2019.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Mixmatch: A holistic approach to semi-supervised learning.Advances in neural information processing systems, 32, 2019

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:46.767391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7fc6004d-4539-489c-821f-6a32a77b482f · outbound

This paper cites Maximum likelihood from incomplete data via the em algorithm.Journal of the royal statistical society: series B (methodological), 39(1):1–22, 1977.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Maximum likelihood from incomplete data via the em algorithm.Journal of the royal statistical society: series B (methodological), 39(1):1–22, 1977

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:46.773136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3465100b-8e40-43a4-9148-4189d5883d65 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:46.779321Z

Source-reported events for the cited work

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Observation cd15aefe-f943-4827-98d0-2cc89e06475a · outbound

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

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Robust loss functions under label noise for deep neural networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:46.785072Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f30f559-952a-4585-80af-3e049a3083df · outbound

This paper cites Training deep neural-networks using a noise adap- tation layer.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Training deep neural-networks using a noise adap- tation layer

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:48.353533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7e795612-ea6e-48ad-91d0-6450eb1b5265 · outbound

This paper cites Weinberger.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Weinberger

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:46.796827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3660e8a-b798-4381-87c2-29fcadd9949c · outbound

This paper cites Co-teaching: Robust training of deep neural networks with extremely noisy labels.Advances in neural information processing systems, 31, 2018.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Co-teaching: Robust training of deep neural networks with extremely noisy labels.Advances in neural information processing systems, 31, 2018

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:46.801855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bfd0a0db-f98e-4f50-bf3f-2ed0c9265a16 · outbound

This paper cites Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis.Medical image analysis, 65:101759, 2020.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis.Medical image analysis, 65:101759, 2020

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:48.280994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d965ebc5-d183-416d-b4c6-3a693daf8004 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Adam: A Method for Stochastic Optimization

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:46.812985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1586f8da-56de-484e-9bbc-5d054f74d66c · outbound

This paper cites Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks, 2013.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks, 2013

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:48.205561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ac516b15-a3af-49c4-af45-f7c4c4fc1bcb · outbound

This paper cites DivideMix: Learning with Noisy Labels as Semi-supervised Learning.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:47.010658Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01b41941-002d-45c8-b894-2fb3082af3d2 · outbound

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

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Making deep neural networks robust to label noise: A loss correction approach

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:47.037711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f6b28d2-664f-4f3c-9abd-5d455e630921 · outbound

This paper cites Deep learning on a data diet: Finding important examples early in training, 2023.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Deep learning on a data diet: Finding important examples early in training, 2023

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:48.165194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3b4ac9e2-cdea-45ac-adcf-637acafa589f · outbound

This paper cites Identifying mislabeled data using the area under the margin ranking.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Identifying mislabeled data using the area under the margin ranking

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:48.107491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d7193d33-585e-4be8-adad-3bfe9bc31d60 · outbound

This paper cites Training Deep Neural Networks on Noisy Labels with Bootstrapping.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Training Deep Neural Networks on Noisy Labels with Bootstrapping

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:47.052411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fdadb5a-5f5a-41af-9194-8b3a14dc2f53 · outbound

This paper cites Dissecting sample hardness: A fine-grained analysis of hardness characterization methods for data-centric AI, 2024.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Dissecting sample hardness: A fine-grained analysis of hardness characterization methods for data-centric AI, 2024

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:47.997533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 848b6568-2dae-4a02-b0fa-e061ef22a23e · outbound

This paper cites Selfie: Refurbishing unclean samples for robust deep learning.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Selfie: Refurbishing unclean samples for robust deep learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:47.980636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e75a5187-fa33-45fc-b1ab-547d4d45f26a · outbound

This paper cites Smith, and Yejin Choi.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Smith, and Yejin Choi

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:47.850002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c1d85822-518a-4ab7-8fec-90685e281588 · outbound

This paper cites an unresolved cited work.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-15T14:48:47.832714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e0fd16af-65ac-49ab-bb1c-587695b310e8 · outbound

This paper cites Proselflc: Progressive self label correction for training robust deep neural networks.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Proselflc: Progressive self label correction for training robust deep neural networks

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:47.301731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93b6fd8b-7749-49c0-bd0e-76cffa2b016b · outbound

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

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Symmetric cross entropy for robust learning with noisy labels

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:47.798010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 30e2bc02-a94f-4b33-ba44-c3325820a303 · outbound

This paper cites Combating noisy labels by agreement: A joint training method with co-regularization.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Combating noisy labels by agreement: A joint training method with co-regularization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:47.753915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8817a9d1-2f61-4958-9b89-1922cb483469 · outbound

This paper cites Sample selection with uncertainty of losses for learning with noisy labels, 2021.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Sample selection with uncertainty of losses for learning with noisy labels, 2021

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:47.712094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8590bc4e-5fe8-4db3-bed2-1813b46a9acf · outbound

This paper cites Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.Scientific Data, 10(1):41, 2023.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.Scientific Data, 10(1):41, 2023

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:47.433477Z

Source-reported events for the cited work

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Observation 24a25789-c813-44fc-a2c7-a8a2b2653369 · outbound

This paper cites Understanding deep learning requires rethinking generalization.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Understanding deep learning requires rethinking generalization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T14:48:47.438442Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 631d530a-97d1-40d5-82fa-357b0d3612fd · outbound

This paper cites Understand- ing deep learning (still) requires rethinking generalization.Communications of the ACM, 64 (3):107–115, 2021.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Understand- ing deep learning (still) requires rethinking generalization.Communications of the ACM, 64 (3):107–115, 2021

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:47.684523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7e77e6de-914b-4930-8862-1cd07f2b736d · outbound

This paper cites Generalized cross entropy loss for training deep neural net- works with noisy labels.Advances in neural information processing systems, 31, 2018.

LiNC: Lightweight Noise Correction via Per-Sample Trust and Gaussian Mixture Modeling Generalized cross entropy loss for training deep neural net- works with noisy labels.Advances in neural information processing systems, 31, 2018

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T14:48:47.612315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.