Paper Citation Record · LEDGER

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 1 inbound Pith citation observation for arXiv:2505.15671.

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

pith.paper-citation-record.v1
2505.15671 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:17:22.079145Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-16T08:35:15.476127Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T08:37:37.204007Z

Reference resolution

33 of 33 outbound references displayed

verified exact0
verified fuzzy20
unresolved12
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87b8acf1-1afa-46f0-86b5-420e4e5fa066 · outbound

This paper cites nature 542(7639), 115–118 (2017).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification nature 542(7639), 115–118 (2017)

Reference 1

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Source-reported events for the cited work

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Observation c6a2c43a-0047-4c40-a53a-ce81971e2d64 · outbound

This paper cites In: 2023 24th International Conference on Digital Signal Processing (DSP), pp.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification In: 2023 24th International Conference on Digital Signal Processing (DSP), pp

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:26.704970Z

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Observation b52f5360-56d2-48a0-940e-85993bfc18ba · outbound

This paper cites Drug discovery today 23(6), 1241–1250 (2018).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Drug discovery today 23(6), 1241–1250 (2018)

Reference 3

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Observation 01222ccc-8a81-4270-a258-7751bbcea518 · outbound

This paper cites Advances in neural information processing systems 25, 1097–1105 (2012).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Advances in neural information processing systems 25, 1097–1105 (2012)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:26.475482Z

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Observation dca2fb1c-f787-472a-9e2b-7a0ffa395f3b · outbound

This paper cites WATT: Weight Average Test-Time Adaptation of CLIP.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification WATT: Weight Average Test-Time Adaptation of CLIP

Reference 5

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Observation 51631b42-fbda-4c9f-b358-65548ac5f1a9 · outbound

This paper cites Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Bayesian Low-Rank LeArning (Bella): A Practical Approach to Bayesian Neural Networks

Reference 6

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Observation a20a3d35-40cb-4ac7-9131-6232501887df · outbound

This paper cites In: Extreme Man-made and Natural Hazards in Dynamics of Structures, pp.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification In: Extreme Man-made and Natural Hazards in Dynamics of Structures, pp

Reference 7

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Observation 995a5ff2-27cd-4286-8285-3644a6150178 · outbound

This paper cites an unresolved cited work.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Unresolved cited work

Reference 8

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Observation 2ccf2f0c-2933-4b2e-85c3-c3b1de1a6e05 · outbound

This paper cites Advances in neural information processing systems 29, 4134–4142 (2016).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Advances in neural information processing systems 29, 4134–4142 (2016)

Reference 9

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Observation 301f5290-3d7e-45e0-8bc5-27289c301e8b · outbound

This paper cites In: International Conference on Machine Learning, pp.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification In: International Conference on Machine Learning, pp

Reference 10

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Observation 925d2c13-ffda-46d1-b23c-22b5457f9861 · outbound

This paper cites Advances in neural information processing systems 24 (2011).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Advances in neural information processing systems 24 (2011)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:25.867367Z

Source-reported events for the cited work

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Observation dd224ba3-04aa-459a-9f6b-234ddfb9e0e3 · outbound

This paper cites In: Proceedings of the 22nd International Conference on Machine Learning, pp.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification In: Proceedings of the 22nd International Conference on Machine Learning, pp

Reference 12

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Observation 708e100b-ab4f-404e-999c-015227169610 · outbound

This paper cites In: International Conference on Machine Learning, pp.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification In: International Conference on Machine Learning, pp

Reference 13

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Observation c1c1e118-d56f-49bd-91d3-4fb51c39c94a · outbound

This paper cites Photogrammetric Engineering & Remote Sensing 82(3), 189–197 (2016).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Photogrammetric Engineering & Remote Sensing 82(3), 189–197 (2016)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:25.455139Z

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Observation 54857548-2113-4126-b1f5-6f3bbd689aa8 · outbound

This paper cites Advances in engi- neering software 69, 46–61 (2014).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Advances in engi- neering software 69, 46–61 (2014)

Reference 15

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Observation 119ffa82-b012-45e5-bbdb-8e46dead3c80 · outbound

This paper cites In: System Modeling and Optimization: Proceedings of the 10th IFIP Conference New York City, USA, August 31–September 4, 1981, pp.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification In: System Modeling and Optimization: Proceedings of the 10th IFIP Conference New York City, USA, August 31–September 4, 1981, pp

Reference 16

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Observation 6ceeb271-1a9d-4005-ac53-1aa7427c4449 · outbound

This paper cites Evolutionary computation 25(1), 1–54 (2017).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Evolutionary computation 25(1), 1–54 (2017)

Reference 17

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Observation ecb55f60-1631-44a5-82f0-0b62d4e414b5 · outbound

This paper cites Kaggle (2021).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Kaggle (2021)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:24.812721Z

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Observation 1215ea2c-cbbe-49c2-8f49-db8cd6cb68e4 · outbound

This paper cites Mathematical Biosciences and Engineering 19(3), 2381– 2402 (2022).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Mathematical Biosciences and Engineering 19(3), 2381– 2402 (2022)

Reference 19

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Observation 95fecad2-4849-471c-9c5f-107f19fefdd6 · outbound

This paper cites https://www.microsoft.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification https://www.microsoft

Reference 20

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Observation 72cd3e23-e70f-4fd8-922a-0635f6b989d7 · outbound

This paper cites In: Biomedical Image Processing and Biomedical Visualization, vol.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification In: Biomedical Image Processing and Biomedical Visualization, vol

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:24.437984Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fbf29c6f-d2cd-44cf-9b5a-55f759a27d1d · outbound

This paper cites The journal of machine learning research 15(1), 1929–1958 (2014).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification The journal of machine learning research 15(1), 1929–1958 (2014)

Reference 22

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Observation 079038ab-285b-42fd-b5d8-fa36e1b21e93 · outbound

This paper cites Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

Reference 23

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Observation bc0af0b2-0835-482c-9552-258b55ac4dd6 · outbound

This paper cites Concrete Dropout.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Concrete Dropout

Reference 24

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Observation 99e0be57-5c65-4356-978a-82e3b0c573b7 · outbound

This paper cites Scientific Reports 12(1), 1–11 (2022).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Scientific Reports 12(1), 1–11 (2022)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:24.288142Z

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Observation ff0abb5b-85b4-43c4-ad82-8458a5bd6177 · outbound

This paper cites In: International Conference on Machine Learning, pp.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification In: International Conference on Machine Learning, pp

Reference 26

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Observation 833a5c64-258a-468e-928e-3d5fbceb960f · outbound

This paper cites Advances in neural information processing systems 30 (2017).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Advances in neural information processing systems 30 (2017)

Reference 27

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Observation b7d0e4a6-45d9-4bec-b4a9-67c3811b2cf6 · outbound

This paper cites An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions

Reference 28

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Observation 3ee8c65c-1e81-449d-b0cd-fd9ca84b8d0a · outbound

This paper cites Neural Computing and Applications 35(30), 22179–22188 (2023).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Neural Computing and Applications 35(30), 22179–22188 (2023)

Reference 29

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Observation 34a7850f-24fd-4ab3-94ae-a3b2fffa1abe · outbound

This paper cites arXiv e-prints (2014).

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification arXiv e-prints (2014)

Reference 30

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Observation e13498ff-d495-4cf8-a268-83ea0d67a88c · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Reference 31

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Observation 87d2668e-b694-4a7b-bcd4-072fb32eef42 · outbound

This paper cites In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:17:22.526127Z

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Observation 4a8fc535-7d18-4851-a546-138415a79851 · outbound

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Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:17:22.348662Z

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Pith citing papers

Observation 10a20a07-adf3-4c89-9b7b-c4c2e4d17e64 · inbound

NeuroPareto: Calibrated Acquisition for Costly Many-Goal Search in Vast Parameter Spaces cites this paper.

NeuroPareto: Calibrated Acquisition for Costly Many-Goal Search in Vast Parameter Spaces Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification

Reference 17

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Source-reported events for the cited work

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