Paper Citation Record · LEDGER

A Simple and Effective Method for Uncertainty Quantification and OOD Detection

As of 11 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2508.00754.

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

pith.paper-citation-record.v1
2508.00754 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T06:01:33.097249Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

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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

45 of 45 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c7a7cb33-67ad-47f0-95c4-cbee3fc5a4c8 · outbound

This paper cites Deep learning,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Deep learning,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.577741Z

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Observation fa69580e-1a19-4b30-9453-ab07bf1c5e3e · outbound

This paper cites ImageNet classification with deep convolutional neural networks,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection ImageNet classification with deep convolutional neural networks,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.567807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9db66aba-52b8-466c-ad11-7c6da7b4042b · outbound

This paper cites A survey of the usages of deep learning for natural language processing,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection A survey of the usages of deep learning for natural language processing,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.558169Z

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No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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This paper cites A survey of deep learning techniques for autonomous driving,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection A survey of deep learning techniques for autonomous driving,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.548751Z

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Observation 6859bf7d-01ff-43c4-848b-a4af6bb5b369 · outbound

This paper cites Human breast numerical model generation based on deep learning for photoacoustic imaging,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Human breast numerical model generation based on deep learning for photoacoustic imaging,

Reference 5

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Observation 4932d26b-ce0c-40b6-8404-0bff5eb7ae31 · outbound

This paper cites BPEN: Brain Posterior Evidential Network for trustworthy brain imaging analysis,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection BPEN: Brain Posterior Evidential Network for trustworthy brain imaging analysis,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.530252Z

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Observation 21a8b6df-d242-4a98-a93b-1b33eb8f9a1c · outbound

This paper cites Recognizing Limits: Investigating Infeasibility in Large Language Models.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Recognizing Limits: Investigating Infeasibility in Large Language Models

Reference 7

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Observation d76d3312-b122-4c7a-a11f-192d9eb370bc · outbound

This paper cites A review of uncertainty quantification in deep learning: Techniques, applications and challenges,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection A review of uncertainty quantification in deep learning: Techniques, applications and challenges,

Reference 8

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Observation 372d83d1-665b-4d6c-8518-1fdee5019562 · outbound

This paper cites Aleatory or epistemic? Does it matter?.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Aleatory or epistemic? Does it matter?

Reference 9

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Observation 0a19bfc2-3ac0-4c6f-8fb0-7f7149b7bcc5 · outbound

This paper cites Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.503478Z

Source-reported events for the cited work

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Observation 7df69adc-c304-4ba9-bb50-1afc4ba1d275 · outbound

This paper cites A survey on un- certainty quantification methods for deep learning,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection A survey on un- certainty quantification methods for deep learning,

Reference 11

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Source-reported events for the cited work

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Observation 3e9e2cb9-88b8-4b07-84b5-be1e1f12045c · outbound

This paper cites A survey of uncertainty in deep neural networks,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection A survey of uncertainty in deep neural networks,

Reference 12

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Observation 96a04909-eb24-4bc9-80e3-8a0d631ae82b · outbound

This paper cites Active learning with statistical models,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Active learning with statistical models,

Reference 13

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Observation c9d7e25d-57c3-4339-a495-890bb4803ba6 · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 14

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Observation 2445179a-f841-4729-b507-8dcabd1cc4d3 · outbound

This paper cites Deep Bayesian Active Learning with Image Data,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Deep Bayesian Active Learning with Image Data,

Reference 15

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Observation 90ec7ccb-1c41-4cab-b6d3-ff91a97c612e · outbound

This paper cites Generalized ODIN: Detecting out-of-distribution image without learning from out-of-distribution data,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Generalized ODIN: Detecting out-of-distribution image without learning from out-of-distribution data,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.467498Z

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Observation a0a73633-8b3a-4289-b37e-feba6cbf18c6 · outbound

This paper cites an unresolved cited work.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Unresolved cited work

Reference 18

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Observation a344f65d-4e80-440c-a090-8b0a3f33eba6 · outbound

This paper cites Bayesian training of backpropagation networks by the hybrid Monte Carlo method,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Bayesian training of backpropagation networks by the hybrid Monte Carlo method,

Reference 19

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Observation 7806b160-dfa8-4ea5-a879-006f26eb42b2 · outbound

This paper cites Transforming neural-net output levels to probability distributions,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Transforming neural-net output levels to probability distributions,

Reference 20

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Observation c8e02c96-3c29-4a59-b85c-6063045b98c9 · outbound

This paper cites A practical Bayesian framework for backpropagation networks,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection A practical Bayesian framework for backpropagation networks,

Reference 21

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Observation 045a45ae-aca0-4c61-965f-a5f9da0d5833 · outbound

This paper cites Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Dropout as a Bayesian approximation: Representing model uncertainty in deep learning,

Reference 22

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Observation daf211c9-539a-44ac-b523-4aa330d4ae44 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Simple and scalable predictive uncertainty estimation using deep ensembles,

Reference 23

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Source-reported events for the cited work

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Observation 2af829d0-82e4-44ad-90a7-0ddc078c7580 · outbound

This paper cites Evidential deep learning to quantify classification uncertainty,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Evidential deep learning to quantify classification uncertainty,

Reference 24

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Source-reported events for the cited work

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Observation 92c74dfb-fd48-4579-a93f-f982b6934bd2 · outbound

This paper cites Predictive uncertainty estimation via prior networks,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Predictive uncertainty estimation via prior networks,

Reference 25

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Source-reported events for the cited work

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Observation f68e8009-4c8d-4d05-8686-31bfd966865c · outbound

This paper cites A simple approach to improve single-model deep uncertainty via distance-awareness,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection A simple approach to improve single-model deep uncertainty via distance-awareness,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.381083Z

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Observation 0f09cfc9-29c7-4ea0-8254-be85350f64e0 · outbound

This paper cites Density-softmax: Efficient test-time model for uncertainty estimation and robustness under distribution shifts,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Density-softmax: Efficient test-time model for uncertainty estimation and robustness under distribution shifts,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.371933Z

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Observation 7e754aa5-0c3a-49ff-8206-f86273a96d31 · outbound

This paper cites Discriminant Distance-Aware Rep- resentation on Deterministic Uncertainty Quantification Methods,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Discriminant Distance-Aware Rep- resentation on Deterministic Uncertainty Quantification Methods,

Reference 28

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Observation 43cfabfe-5d48-43f1-b62b-1d08cea00731 · outbound

This paper cites Uncertainty estimation using a single deep deterministic neural network,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Uncertainty estimation using a single deep deterministic neural network,

Reference 29

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Observation 6e413d52-032a-4406-bd27-33c2bc566954 · outbound

This paper cites Simple and principled uncertainty estimation with deterministic deep learning via distance awareness,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Simple and principled uncertainty estimation with deterministic deep learning via distance awareness,

Reference 30

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Observation bdb345b9-ee2f-45f6-bf95-c151a91dd22b · outbound

This paper cites Deep deterministic uncertainty: A new simple baseline,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Deep deterministic uncertainty: A new simple baseline,

Reference 31

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Observation bf72aaf1-2573-4e0c-9955-767fc6bca6d5 · outbound

This paper cites A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks

Reference 32

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Observation 3539c6ca-f6a6-44d2-9ad6-117ca52da398 · outbound

This paper cites Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks

Reference 33

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Observation 617fef37-bc73-4821-a127-bae311993184 · outbound

This paper cites A simple unified framework for detecting out-of-distribution samples and adversarial attacks,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection A simple unified framework for detecting out-of-distribution samples and adversarial attacks,

Reference 34

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Observation 0ac1638a-5f23-48b1-81f4-6e034c2358b9 · outbound

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A Simple and Effective Method for Uncertainty Quantification and OOD Detection Energy-based out-of-distribution detection,

Reference 35

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Observation 473bd11b-e2b0-4d19-ad1b-a1b2211aee6e · outbound

This paper cites On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty

Reference 36

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A Simple and Effective Method for Uncertainty Quantification and OOD Detection Unresolved cited work

Reference 37

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Observation fee73178-8b05-47ca-9790-72ceaa90402d · outbound

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

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Learning multiple layers of features from tiny images,

Reference 38

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Observation f259fe5d-4ec5-44dd-81ab-2fd036ce041e · outbound

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A Simple and Effective Method for Uncertainty Quantification and OOD Detection Reading digits in natural images with unsupervised feature learning,

Reference 39

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A Simple and Effective Method for Uncertainty Quantification and OOD Detection Unresolved cited work

Reference 40

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Observation 4cba0d24-d369-4cf3-a85a-c033c28c7044 · outbound

This paper cites Obtaining well calibrated probabilities using Bayesian binning,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Obtaining well calibrated probabilities using Bayesian binning,

Reference 41

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Observation a7acb9a7-4a74-4cd5-8d5f-3f648cf32837 · outbound

This paper cites Deep residual learning for image recognition,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Deep residual learning for image recognition,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.259559Z

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Observation 96675fb1-71bc-4bf5-b2fb-2c90bb857951 · outbound

This paper cites Wide Residual Networks.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Wide Residual Networks

Reference 43

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A Simple and Effective Method for Uncertainty Quantification and OOD Detection Visualizing data using t-SNE,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.249747Z

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Observation 49b9f23d-4df8-45ba-a159-aeca59509687 · outbound

This paper cites Measures of entropy from data using infinitely divisible kernels,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Measures of entropy from data using infinitely divisible kernels,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.239842Z

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Observation cddaa763-cb75-4c29-8faa-112fe929a3db · outbound

This paper cites Understanding autoencoders with information theoretic concepts,.

A Simple and Effective Method for Uncertainty Quantification and OOD Detection Understanding autoencoders with information theoretic concepts,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:01:48.229983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.