Paper Citation Record · LEDGER

Epistemic Wrapping for Uncertainty Quantification

As of 18 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 2 inbound Pith citation observations for arXiv:2505.02277.

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

pith.paper-citation-record.v1
2505.02277 v2

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T01:05:21.513470Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:21:13.909858Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T05:17:18.203336Z

Reference resolution

54 of 54 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 21c9611f-7640-4e36-aee2-97a77480d4f9 · outbound

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

Epistemic Wrapping for Uncertainty Quantification A review of uncertainty quantification in deep learning: Techniques, applications and challenges

Reference 1

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Observation 55b47809-4c13-48be-bb2d-b3513115d3db · outbound

This paper cites Variational inference: A review for statisticians.

Epistemic Wrapping for Uncertainty Quantification Variational inference: A review for statisticians

Reference 2

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Source-reported events for the cited work

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Observation 6d810abf-02a8-4a8e-b30a-47539cb95075 · outbound

This paper cites Weight uncertainty in neural network.

Epistemic Wrapping for Uncertainty Quantification Weight uncertainty in neural network

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:22.382312Z

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Observation 4a1c95fb-e4d7-4726-a4ab-e31b34fcaffc · outbound

This paper cites Credal Learning Theory.

Epistemic Wrapping for Uncertainty Quantification Credal Learning Theory

Reference 4

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Source-reported events for the cited work

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Observation 7954e62e-d5cb-4ac5-b3e9-2319fe34b319 · outbound

This paper cites Molina, and Christopher Metzler.

Epistemic Wrapping for Uncertainty Quantification Molina, and Christopher Metzler

Reference 5

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Observation 252aa9f5-80f9-43eb-977e-2cd072c504ac · outbound

This paper cites Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts.

Epistemic Wrapping for Uncertainty Quantification Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:22.354130Z

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Observation 47ab3659-850b-436f-b402-57422ae5f5da · outbound

This paper cites On the credal structure of consistent probabilities.

Epistemic Wrapping for Uncertainty Quantification On the credal structure of consistent probabilities

Reference 7

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Source-reported events for the cited work

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Observation 173b303d-a46c-4b33-849d-d48cad2e8b68 · outbound

This paper cites Generalised max entropy classifiers.

Epistemic Wrapping for Uncertainty Quantification Generalised max entropy classifiers

Reference 8

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Source-reported events for the cited work

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Observation 0e5970e3-4127-4bc5-8311-12300d33f2e1 · outbound

This paper cites The geometry of uncertainty: The geometry of imprecise probabilities.

Epistemic Wrapping for Uncertainty Quantification The geometry of uncertainty: The geometry of imprecise probabilities

Reference 9

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Observation 98f7f0cd-cfa8-4afe-83bf-64e3505868f2 · outbound

This paper cites Uncertainty measures: The big picture.

Epistemic Wrapping for Uncertainty Quantification Uncertainty measures: The big picture

Reference 10

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Observation f267a38d-d9f1-483c-94a6-3ba593a62ce1 · outbound

This paper cites Uncertainty measures: A critical survey.

Epistemic Wrapping for Uncertainty Quantification Uncertainty measures: A critical survey

Reference 11

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Source-reported events for the cited work

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Observation c2f8e144-83f5-4217-a2ef-1f46682324fe · outbound

This paper cites Belief modeling regression for pose estimation.

Epistemic Wrapping for Uncertainty Quantification Belief modeling regression for pose estimation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:22.266630Z

Source-reported events for the cited work

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Observation 5e88224e-dcc9-4b4a-820e-1494bd9a0bec · outbound

This paper cites Upper and lower probabilities induced by a multivalued mapping.

Epistemic Wrapping for Uncertainty Quantification Upper and lower probabilities induced by a multivalued mapping

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:22.251042Z

Source-reported events for the cited work

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Observation bd3aed92-ac25-4b2d-8a17-ebd66564bd91 · outbound

This paper cites Large scale structure of neural network loss landscapes.

Epistemic Wrapping for Uncertainty Quantification Large scale structure of neural network loss landscapes

Reference 14

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Source-reported events for the cited work

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Observation b87127c0-28eb-461c-8f18-eb1629308c56 · outbound

This paper cites Energy-based epistemic uncertainty for graph neural networks.

Epistemic Wrapping for Uncertainty Quantification Energy-based epistemic uncertainty for graph neural networks

Reference 15

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Observation e62a58b2-e96c-4e14-961e-9f4770c40832 · outbound

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

Epistemic Wrapping for Uncertainty Quantification Dropout as a Bayesian approximation: Representing model uncertainty in deep learning

Reference 16

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Source-reported events for the cited work

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Observation c7e64e35-a956-4312-8027-fad1844b7596 · outbound

This paper cites A belief-theoretical approach to example-based pose estimation.

Epistemic Wrapping for Uncertainty Quantification A belief-theoretical approach to example-based pose estimation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:22.198818Z

Source-reported events for the cited work

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Observation be6e8b35-0228-4466-b62c-c3032820b6c5 · outbound

This paper cites Interval arithmetic: From principles to implementation.

Epistemic Wrapping for Uncertainty Quantification Interval arithmetic: From principles to implementation

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:22.182591Z

Source-reported events for the cited work

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Observation ba9165a0-ddd9-4a56-b210-5862bd7b09c4 · outbound

This paper cites The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo.

Epistemic Wrapping for Uncertainty Quantification The No-U-Turn sampler: Adaptively setting path lengths in Hamiltonian Monte Carlo

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:22.165177Z

Source-reported events for the cited work

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Observation 31ed5812-2796-4ffe-8035-d7e50b353ceb · outbound

This paper cites Quantifying aleatoric and epistemic uncertainty: A credal approach.

Epistemic Wrapping for Uncertainty Quantification Quantifying aleatoric and epistemic uncertainty: A credal approach

Reference 20

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Observation 00a5a9b0-264d-48ff-911f-3da266adb1a7 · outbound

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Epistemic Wrapping for Uncertainty Quantification Unresolved cited work

Reference 21

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Observation 8c02d88d-3a6e-449d-8e9d-b8b170578fd6 · outbound

This paper cites Aleatoric and epistemic uncertainty in machine learning: An introduc- tion to concepts and methods.

Epistemic Wrapping for Uncertainty Quantification Aleatoric and epistemic uncertainty in machine learning: An introduc- tion to concepts and methods

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:22.110202Z

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Observation cf0e4939-37cb-4fc4-bcbb-adc0117ea335 · outbound

This paper cites Hands-on Bayesian neural networks—A tutorial for deep learning users.

Epistemic Wrapping for Uncertainty Quantification Hands-on Bayesian neural networks—A tutorial for deep learning users

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:22.092392Z

Source-reported events for the cited work

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Observation 3254dca4-b80a-4066-b1d8-9e522822882d · outbound

This paper cites Is epistemic uncertainty faithfully represented by evidential deep learning methods? In Forty-first International Conference on Machine Learning, 2024.

Epistemic Wrapping for Uncertainty Quantification Is epistemic uncertainty faithfully represented by evidential deep learning methods? In Forty-first International Conference on Machine Learning, 2024

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:22.074483Z

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Observation 5df9870e-17e8-4714-b336-24110266798f · outbound

This paper cites Understanding the difficulty of training deep feedforward neural networks xavier.

Epistemic Wrapping for Uncertainty Quantification Understanding the difficulty of training deep feedforward neural networks xavier

Reference 25

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Observation bbf49d9f-2ab3-4b42-b924-ac7c8134d5f8 · outbound

This paper cites Auto-Encoding Variational Bayes.

Epistemic Wrapping for Uncertainty Quantification Auto-Encoding Variational Bayes

Reference 26

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Observation 1412a8ac-82a9-47a6-915f-141ec8a4569e · outbound

This paper cites CIFAR-10 (Canadian Institute For Advanced Research).

Epistemic Wrapping for Uncertainty Quantification CIFAR-10 (Canadian Institute For Advanced Research)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:22.033822Z

Source-reported events for the cited work

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Observation 02fd96aa-5598-4d9d-8365-b815e42c4bd2 · outbound

This paper cites Reliable confidence measures for medical diagnosis with evolutionary algorithms.

Epistemic Wrapping for Uncertainty Quantification Reliable confidence measures for medical diagnosis with evolutionary algorithms

Reference 28

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Observation 32c6b50a-65b7-4937-827e-eeb746618b83 · outbound

This paper cites The MNIST database of handwritten digits.

Epistemic Wrapping for Uncertainty Quantification The MNIST database of handwritten digits

Reference 29

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Observation ead98242-362f-482f-b1d8-9742723bb5ee · outbound

This paper cites The enterprise of knowledge: An essay on knowledge, credal probability, and chance.

Epistemic Wrapping for Uncertainty Quantification The enterprise of knowledge: An essay on knowledge, credal probability, and chance

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.979953Z

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Observation 182e169f-0644-4124-bf61-4853c269b086 · outbound

This paper cites Graph neural stochastic diffusion for estimating uncertainty in node classification.

Epistemic Wrapping for Uncertainty Quantification Graph neural stochastic diffusion for estimating uncertainty in node classification

Reference 31

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Observation ca9bfa1d-13e7-48b7-86e8-91410e400134 · outbound

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

Epistemic Wrapping for Uncertainty Quantification Simple and principled uncertainty estimation with deterministic deep learning via distance awareness

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.948320Z

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Observation 48665062-54a3-4917-87e2-68d1d949aa11 · outbound

This paper cites Evidence combination based on credal belief redistribution for pattern classification.

Epistemic Wrapping for Uncertainty Quantification Evidence combination based on credal belief redistribution for pattern classification

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.931207Z

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Observation e4baca46-be71-451f-88a6-7c4fdf1d7d40 · outbound

This paper cites Ensemble Distribution Distillation.

Epistemic Wrapping for Uncertainty Quantification Ensemble Distribution Distillation

Reference 34

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Source-reported events for the cited work

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Observation 2814e04a-2fff-4381-a96f-0f901c00294b · outbound

This paper cites Epistemic Deep Learning.

Epistemic Wrapping for Uncertainty Quantification Epistemic Deep Learning

Reference 35

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Source-reported events for the cited work

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Observation 8c05c438-4183-443b-beab-4554585ba5f6 · outbound

This paper cites A unified evaluation framework for epistemic predictions, 2025.

Epistemic Wrapping for Uncertainty Quantification A unified evaluation framework for epistemic predictions, 2025

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.912868Z

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Observation b51dab76-2a0c-4229-b814-c223c05661be · outbound

This paper cites Random-Set Neural Networks (RS-NN).

Epistemic Wrapping for Uncertainty Quantification Random-Set Neural Networks (RS-NN)

Reference 37

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Source-reported events for the cited work

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Observation 147ba8cd-b52a-44f6-b378-8fca853fc903 · outbound

This paper cites Random-set neural networks.

Epistemic Wrapping for Uncertainty Quantification Random-set neural networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.894049Z

Source-reported events for the cited work

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Observation 9cce2f82-31f5-4548-9066-a43b67fc7206 · outbound

This paper cites Variational dropout sparsifies deep neural networks.

Epistemic Wrapping for Uncertainty Quantification Variational dropout sparsifies deep neural networks

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.875572Z

Source-reported events for the cited work

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Observation 18d6965d-6863-4444-8fab-dde08776436e · outbound

This paper cites Adaptive sampling to reduce epistemic uncertainty using prediction interval-generation neural networks.

Epistemic Wrapping for Uncertainty Quantification Adaptive sampling to reduce epistemic uncertainty using prediction interval-generation neural networks

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.855848Z

Source-reported events for the cited work

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Observation b481dac9-6f01-41d2-9107-c3634501b040 · outbound

This paper cites MCMC using Hamiltonian dynamics.

Epistemic Wrapping for Uncertainty Quantification MCMC using Hamiltonian dynamics

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.838760Z

Source-reported events for the cited work

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Observation a9a59a63-3d7f-4df6-86d8-d41632f9c4a8 · outbound

This paper cites Practical Deep Learning with Bayesian Principles.

Epistemic Wrapping for Uncertainty Quantification Practical Deep Learning with Bayesian Principles

Reference 42

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Source-reported events for the cited work

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Observation bf14246f-116a-4818-a9da-792a66e88911 · outbound

This paper cites Is the volume of a credal set a good measure for epistemic uncertainty? In Uncertainty in Artificial Intelligence, pages 1795–1804.

Epistemic Wrapping for Uncertainty Quantification Is the volume of a credal set a good measure for epistemic uncertainty? In Uncertainty in Artificial Intelligence, pages 1795–1804

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.824037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b0836075-bfbf-495d-9f86-9165e452c95d · outbound

This paper cites Evidential deep learning to quantify classification uncertainty.

Epistemic Wrapping for Uncertainty Quantification Evidential deep learning to quantify classification uncertainty

Reference 44

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Source-reported events for the cited work

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Observation 426e9d41-48a3-4886-bfa0-eef661a2e554 · outbound

This paper cites A mathematical theory of evidence, volume 42.

Epistemic Wrapping for Uncertainty Quantification A mathematical theory of evidence, volume 42

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.798440Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 15b293e0-ea93-4b9b-b539-e395e067c62d · outbound

This paper cites The transferable belief model and other interpretations of Dempster–Shafer’s model.

Epistemic Wrapping for Uncertainty Quantification The transferable belief model and other interpretations of Dempster–Shafer’s model

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.784447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation df205c39-e5c2-42ad-964e-f9af4a6a2dd9 · outbound

This paper cites Belief functions on real numbers.International Journal of Approximate Reasoning, 40(3):181–223, 2005.

Epistemic Wrapping for Uncertainty Quantification Belief functions on real numbers.International Journal of Approximate Reasoning, 40(3):181–223, 2005

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.767958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b6459499-888e-48ac-a70a-344ebc3a1129 · outbound

This paper cites Thiagarajan.

Epistemic Wrapping for Uncertainty Quantification Thiagarajan

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.751896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6c05737d-56ce-4839-be57-e96bc9799d2d · outbound

This paper cites Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification.

Epistemic Wrapping for Uncertainty Quantification Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T01:05:21.492096Z

Source-reported events for the cited work

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Observation d069014d-450c-4e2f-add5-64ef6a03ca37 · outbound

This paper cites Credal deep ensembles for uncertainty quantification.

Epistemic Wrapping for Uncertainty Quantification Credal deep ensembles for uncertainty quantification

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.737677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 495ed730-4d75-49c2-b884-35d5adc8227c · outbound

This paper cites Creinns: Credal-set interval neural networks for uncertainty estimation in classification tasks.

Epistemic Wrapping for Uncertainty Quantification Creinns: Credal-set interval neural networks for uncertainty estimation in classification tasks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.723469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1d85f953-49c6-44a6-959e-1970196b53ef · outbound

This paper cites Wasserman.

Epistemic Wrapping for Uncertainty Quantification Wasserman

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.709961Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 25695763-06dc-4bee-9138-87778e92be1e · outbound

This paper cites Flipout: Efficient pseudo-independent weight perturbations on mini-batches.

Epistemic Wrapping for Uncertainty Quantification Flipout: Efficient pseudo-independent weight perturbations on mini-batches

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:05:21.694728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1c9d3a1e-1b42-447e-8f02-6172cbeda801 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Epistemic Wrapping for Uncertainty Quantification Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T01:05:21.513470Z

Source-reported events for the cited work

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Pith citing papers

Observation 3827677a-ceaa-4cd6-89ea-2431d23cef0f · inbound

Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know' cites this paper.

Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know' Epistemic Wrapping for Uncertainty Quantification

Reference 226

Resolution: unresolved; no resolver link, observed 2026-08-15T23:21:13.909858Z

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Observation f05eeb08-bf42-4ea5-9be3-09b282848554 · inbound

Random-Set Graph Neural Networks cites this paper.

Random-Set Graph Neural Networks Epistemic Wrapping for Uncertainty Quantification

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-13T05:17:18.205548Z

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