Paper Citation Record · LEDGER

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning

As of 9 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2508.00922.

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

pith.paper-citation-record.v1
2508.00922 v1

Coverage vector

measured 47 of 47 reference resolution

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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

Source: cited_works

Reference resolution

47 of 47 outbound references displayed

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External citation measurements

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Outbound references

Observation 4b3568e6-61eb-42b9-a201-10e1e193384c · outbound

This paper cites Safe semi- supervised learning using a bayesian neural network.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Safe semi- supervised learning using a bayesian neural network

Reference 1

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Observation 7ede5dc3-ae9d-4d12-86b3-1bd83a8429a0 · outbound

This paper cites Mixmatch: A holistic approach to semi-supervised learning.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Mixmatch: A holistic approach to semi-supervised learning

Reference 2

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Observation 313e75bc-e8ac-4080-80c0-823ea5a6a26d · outbound

This paper cites Cubuk, Alex Ku- rakin, Kihyuk Sohn, Han Zhang, and Colin Raffel.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Cubuk, Alex Ku- rakin, Kihyuk Sohn, Han Zhang, and Colin Raffel

Reference 3

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Observation bb878245-7797-4212-891a-f0b8973fd4b5 · outbound

This paper cites Semi-supervised learning under class distribution mismatch.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Semi-supervised learning under class distribution mismatch

Reference 4

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Observation c7a4428b-7cc4-4395-ab8a-a208037a617d · outbound

This paper cites Boosting semi- supervised learning by exploiting all unlabeled data.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Boosting semi- supervised learning by exploiting all unlabeled data

Reference 5

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Observation 7d982330-c1be-4d56-b8b5-1a883314fc4e · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Imagenet: A large-scale hierarchical image database

Reference 6

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Observation 4313c477-fb42-4aa6-b9fd-ef0d5d063b00 · outbound

This paper cites On calibration of modern neural networks.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning On calibration of modern neural networks

Reference 7

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Observation 5d3516f0-e8f3-407b-bf9d-57d3c3f54a02 · outbound

This paper cites Deep residual learning for image recognition.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Deep residual learning for image recognition

Reference 8

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Observation 10fa58cf-8584-4c17-b9ad-ddf8d6ea15b9 · outbound

This paper cites Safe-student for safe deep semi-supervised learn- ing with unseen-class unlabeled data.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Safe-student for safe deep semi-supervised learn- ing with unseen-class unlabeled data

Reference 9

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Observation 1a11885f-4603-475e-9bc4-53201b68429b · outbound

This paper cites Semi-supervised learning with deep gen- erative models.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Semi-supervised learning with deep gen- erative models

Reference 10

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Observation 8d5a7d68-00c1-4380-b449-d5b5eeadf4f7 · outbound

This paper cites Learning multiple layers of features from tiny images.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Learning multiple layers of features from tiny images

Reference 11

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Observation e96b7d18-93e6-4689-8577-3fd53e50b628 · outbound

This paper cites Tiny imagenet visual recognition challenge.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Tiny imagenet visual recognition challenge

Reference 12

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Observation 4e34457c-a73b-4847-bfd8-9662ee16975a · outbound

This paper cites Pseudo-label: The simple and efﬁ- cient semi-supervised learning method for deep neural net- works.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Pseudo-label: The simple and efﬁ- cient semi-supervised learning method for deep neural net- works

Reference 13

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Observation c3152831-4a38-40cb-83b0-cdaed7a9fa78 · outbound

This paper cites Iomatch: Simplifying open-set semi-supervised learning with joint inliers and outliers utilization.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Iomatch: Simplifying open-set semi-supervised learning with joint inliers and outliers utilization

Reference 14

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Observation a7d627d4-4442-4dc1-9c1a-c1db6fe202e5 · outbound

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CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Unresolved cited work

Reference 15

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Observation 25939cfe-cb27-4832-a164-e35b5777c0df · outbound

This paper cites The devil is in the margin: Margin-based label smooth- ing for network calibration.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning The devil is in the margin: Margin-based label smooth- ing for network calibration

Reference 16

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Observation 2b622102-e629-4a10-8d17-947eacda534e · outbound

This paper cites Revisiting the calibration of modern neu- ral networks.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Revisiting the calibration of modern neu- ral networks

Reference 17

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Observation ece31689-e4f6-4e5e-8df1-d89a146d85fc · outbound

This paper cites When does label smoothing help? Advances in Neural In- formation Processing Systems, 32, 2019.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning When does label smoothing help? Advances in Neural In- formation Processing Systems, 32, 2019

Reference 18

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Observation dd680770-393d-4c64-83fb-32de6056ceb5 · outbound

This paper cites Obtaining well-calibrated probabilities using bayesian binning.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Obtaining well-calibrated probabilities using bayesian binning

Reference 19

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Observation df420070-800f-4dad-b140-89454aaa8aa0 · outbound

This paper cites Reading digits in natural images with unsupervised feature learning.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Reading digits in natural images with unsupervised feature learning

Reference 20

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Observation e5dd974a-5fe4-44e9-ba6b-c5701602a09f · outbound

This paper cites Predicting good probabilities with supervised learning.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Predicting good probabilities with supervised learning

Reference 21

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Observation ce1638cd-b911-4d52-8b17-6c23aa9f3af6 · outbound

This paper cites Rankmixup: Ranking-based mixup training for net- work calibration.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Rankmixup: Ranking-based mixup training for net- work calibration

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.849889Z

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Observation e518d1e8-997a-4f01-aad7-9890dd83ed82 · outbound

This paper cites Realistic evaluation of deep semi-supervised learning algorithms.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Realistic evaluation of deep semi-supervised learning algorithms

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.841985Z

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Observation 0e99f5da-589b-477d-9e2a-9125bdbad25c · outbound

This paper cites Open- match: Open-set semi-supervised learning with open-set consistency regularization.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Open- match: Open-set semi-supervised learning with open-set consistency regularization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.834023Z

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Observation 9d8fc9d8-7c17-4744-9df2-0a5e3cfd71f9 · outbound

This paper cites Flexible distribu- tion alignment: Towards long-tailed semi-supervised learn- 9 ing with proper calibration.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Flexible distribu- tion alignment: Towards long-tailed semi-supervised learn- 9 ing with proper calibration

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.825798Z

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Observation 6eea6470-21a5-44a2-8449-c8d82520eeb8 · outbound

This paper cites Fixmatch: Simpli- fying semi-supervised learning with consistency and conﬁ- dence.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Fixmatch: Simpli- fying semi-supervised learning with consistency and conﬁ- dence

Reference 26

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Observation 4bff0279-9f1c-410d-85cd-c271940b2d9a · outbound

This paper cites On mixup train- ing: Improved calibration and predictive uncertainty for deep neural networks.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning On mixup train- ing: Improved calibration and predictive uncertainty for deep neural networks

Reference 27

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Observation d980ac4c-043a-48b2-a0fe-fc63ecb42884 · outbound

This paper cites Post-hoc uncer- tainty calibration for domain drift scenarios.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Post-hoc uncer- tainty calibration for domain drift scenarios

Reference 28

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Observation b4bf83a0-3f5c-4e44-a014-f4a6ce5428f6 · outbound

This paper cites Scomatch: Alleviating overtrusting in open-set semi-supervised learning.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Scomatch: Alleviating overtrusting in open-set semi-supervised learning

Reference 29

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Observation 48bd1a40-cb9a-45a1-a4d1-d9f69976b150 · outbound

This paper cites Towards realistic long-tailed semi- supervised learning: Consistency is all you need.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Towards realistic long-tailed semi- supervised learning: Consistency is all you need

Reference 30

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Observation 05086333-1552-464d-8f8d-9f91dbcbceac · outbound

This paper cites Vime: Extending the success of self-and semi-supervised learning to tabular domain.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Vime: Extending the success of self-and semi-supervised learning to tabular domain

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.774232Z

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Observation 68791760-2d41-460f-bc76-582102616ecb · outbound

This paper cites Multi- task curriculum framework for open-set semi-supervised learning.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Multi- task curriculum framework for open-set semi-supervised learning

Reference 32

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Observation f377ca96-7074-4922-b6a9-8719a0eab56c · outbound

This paper cites Wide residual net- works.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Wide residual net- works

Reference 33

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Observation 711c965d-bef1-4183-9ddc-9ba52b79653f · outbound

This paper cites On the Convergence of SGD with Biased Gradients.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning On the Convergence of SGD with Biased Gradients

Reference 34

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Observation 7b8aef5b-1822-455e-89f6-878e079890f6 · outbound

This paper cites Randaugment: Practical automated data augmen- tation with a reduced search space.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Randaugment: Practical automated data augmen- tation with a reduced search space

Reference 35

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Observation b6c001a2-04ac-4ba1-9714-1c28043e8627 · outbound

This paper cites Deep residual learning for image recognition.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Deep residual learning for image recognition

Reference 36

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Source-reported events for the cited work

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Observation 673e356d-496b-454d-9183-c80587806179 · outbound

This paper cites Densely connected convolutional net- works.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Densely connected convolutional net- works

Reference 37

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Observation 131ae540-0725-4f8b-8603-dfa10002bae6 · outbound

This paper cites Iomatch: Simplifying open-set semi-supervised learning with joint inliers and outliers utilization.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Iomatch: Simplifying open-set semi-supervised learning with joint inliers and outliers utilization

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.726280Z

Source-reported events for the cited work

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Observation 78f95d96-03f1-4991-93d6-6ec01b368f6a · outbound

This paper cites The devil is in the margin: Margin-based label smooth- ing for network calibration.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning The devil is in the margin: Margin-based label smooth- ing for network calibration

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.717800Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0ad1ad32-e97b-4d3d-9339-a97086154a67 · outbound

This paper cites Rankmixup: Ranking-based mixup training for net- work calibration.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Rankmixup: Ranking-based mixup training for net- work calibration

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.709117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation baf371f1-c52d-4dc9-b12e-def0533decf7 · outbound

This paper cites Realistic evaluation of deep semi-supervised learning algorithms.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Realistic evaluation of deep semi-supervised learning algorithms

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.700321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 9b4d51e1-5cf4-4c29-bd81-0895809dbefc · outbound

This paper cites Open- match: Open-set semi-supervised learning with open-set consistency regularization.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Open- match: Open-set semi-supervised learning with open-set consistency regularization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.691450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 81deddc8-d45b-42fb-a23e-cfafb0a7ffb9 · outbound

This paper cites Flexible distribu- tion alignment: Towards long-tailed semi-supervised learn- ing with proper calibration.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Flexible distribu- tion alignment: Towards long-tailed semi-supervised learn- ing with proper calibration

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.682327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b62f0d81-4d92-4e0b-b8f3-800eda9606aa · outbound

This paper cites Fixmatch: Simpli- fying semi-supervised learning with consistency and conﬁ- dence.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Fixmatch: Simpli- fying semi-supervised learning with consistency and conﬁ- dence

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.671432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4a93c49c-ea64-42e8-a87d-6b81faff4108 · outbound

This paper cites Scomatch: Alleviating overtrusting in open-set semi-supervised learning.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Scomatch: Alleviating overtrusting in open-set semi-supervised learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.661899Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 334fc5cf-ca7f-4ce1-acb6-a56b7bdaf78e · outbound

This paper cites Multi- task curriculum framework for open-set semi-supervised learning.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Multi- task curriculum framework for open-set semi-supervised learning

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.652497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e5a26606-179b-4bb1-ba27-08ee65d80026 · outbound

This paper cites Wide residual net- works.

CaliMatch: Adaptive Calibration for Improving Safe Semi-supervised Learning Wide residual net- works

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:48:10.642530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.