Paper Citation Record · LEDGER

Risk-aware Classification via Uncertainty Quantification

As of 20 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 1 inbound Pith citation observation for arXiv:2412.03391.

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

pith.paper-citation-record.v1
2412.03391 v1

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:34:27.820822Z

measured 68 of 68 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:38:30.129473Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T00:15:52.136695Z

Reference resolution

67 of 67 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation af13163f-0143-44ab-a372-b528251345d8 · outbound

This paper cites Abadi, P.

Risk-aware Classification via Uncertainty Quantification Abadi, P

Reference 1

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Observation 1d1e25d8-0bf0-466d-81cf-cd7055a6e3c4 · outbound

This paper cites Barf: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification.

Risk-aware Classification via Uncertainty Quantification Barf: A new direct and cross-based binary residual feature fusion with uncertainty-aware module for medical image classification

Reference 2

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Observation c221c1cf-e2c3-4be9-b6d9-d79f8a31fe86 · outbound

This paper cites Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi.

Risk-aware Classification via Uncertainty Quantification Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi

Reference 3

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Observation c2640d08-b668-4c1a-b582-b1cdce2f4775 · outbound

This paper cites Uncertaintyfusenet: robust uncertainty-aware hierarchical feature fusion model with ensemble monte carlo dropout for covid-19 detection.

Risk-aware Classification via Uncertainty Quantification Uncertaintyfusenet: robust uncertainty-aware hierarchical feature fusion model with ensemble monte carlo dropout for covid-19 detection

Reference 4

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Observation 54f6c274-0265-4df7-a075-258ee64ee25e · outbound

This paper cites Deep evidential regression.

Risk-aware Classification via Uncertainty Quantification Deep evidential regression

Reference 5

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Observation 846369f8-2ad7-4648-b2b6-0a64ae85c922 · outbound

This paper cites Blundell, J.

Risk-aware Classification via Uncertainty Quantification Blundell, J

Reference 6

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Observation 0adcaa7d-5959-4c6a-96cc-f0ccd28b9599 · outbound

This paper cites Deep, spatially coherent inverse sensor models with uncertainty incorporation using the evidential framework.

Risk-aware Classification via Uncertainty Quantification Deep, spatially coherent inverse sensor models with uncertainty incorporation using the evidential framework

Reference 7

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Observation 7a42fed8-06ac-4725-853d-f8c99c811b82 · outbound

This paper cites Evidential deep learning for open set action recognition.

Risk-aware Classification via Uncertainty Quantification Evidential deep learning for open set action recognition

Reference 8

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This paper cites Kaplan, and Murat S ensoy.

Risk-aware Classification via Uncertainty Quantification Kaplan, and Murat S ensoy

Reference 9

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Observation 0325465b-865a-452a-aa63-772a4819d4c5 · outbound

This paper cites Cost-aware pre-training for multiclass cost-sensitive deep learning.

Risk-aware Classification via Uncertainty Quantification Cost-aware pre-training for multiclass cost-sensitive deep learning

Reference 10

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Observation 2d7fe56c-ef1a-4925-b3cc-698e08395651 · outbound

This paper cites Dempster.

Risk-aware Classification via Uncertainty Quantification Dempster

Reference 11

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Observation c26c9216-429a-483c-a986-5a4690d8262d · outbound

This paper cites A definition of subjective possibility.

Risk-aware Classification via Uncertainty Quantification A definition of subjective possibility

Reference 12

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This paper cites The foundations of cost-sensitive learning.

Risk-aware Classification via Uncertainty Quantification The foundations of cost-sensitive learning

Reference 13

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Observation da21f392-8e7f-4303-86fa-9f344eec13d9 · outbound

This paper cites Introduction to the dirichlet distribution and related processes.

Risk-aware Classification via Uncertainty Quantification Introduction to the dirichlet distribution and related processes

Reference 14

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Observation cb4e6c57-d70e-4947-8c1f-3f05644d9577 · outbound

This paper cites Exploring the limits of out-of-distribution detection.

Risk-aware Classification via Uncertainty Quantification Exploring the limits of out-of-distribution detection

Reference 15

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Observation a0cc9832-a879-4164-871c-df5f6188b1e2 · outbound

This paper cites Dense out-of-distribution detection by robust learning on synthetic negative data.

Risk-aware Classification via Uncertainty Quantification Dense out-of-distribution detection by robust learning on synthetic negative data

Reference 16

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Observation 869fadb9-50d0-4567-ad51-aaa0a12b4384 · outbound

This paper cites Cost-sensitive regularization for diabetic retinopathy grading from eye fundus images.

Risk-aware Classification via Uncertainty Quantification Cost-sensitive regularization for diabetic retinopathy grading from eye fundus images

Reference 17

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Observation 7ac23687-fad3-45be-a897-1f703e5333e0 · outbound

This paper cites Gal and Z.

Risk-aware Classification via Uncertainty Quantification Gal and Z

Reference 18

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Observation 67c66e6f-8ff9-41ec-93e7-a69c5a836836 · outbound

This paper cites Quantifying and leveraging classification uncertainty for chest radiograph assessment.

Risk-aware Classification via Uncertainty Quantification Quantifying and leveraging classification uncertainty for chest radiograph assessment

Reference 19

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Observation 994a7520-eb09-4b2a-8e74-5f2af106bcd7 · outbound

This paper cites On calibration of modern neural networks.

Risk-aware Classification via Uncertainty Quantification On calibration of modern neural networks

Reference 20

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Observation d546eb3b-f1ff-4310-9d02-2fe1401ae994 · outbound

This paper cites Kaplan, Audun Josang, Dong H.

Risk-aware Classification via Uncertainty Quantification Kaplan, Audun Josang, Dong H

Reference 21

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Observation e5ccda50-545b-4b1e-b9f5-d1478e4e850d · outbound

This paper cites Uncertainty-aware reliable text classification.

Risk-aware Classification via Uncertainty Quantification Uncertainty-aware reliable text classification

Reference 22

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Observation 22d13b79-bd94-4310-8d43-91bf2037f529 · outbound

This paper cites u hl, and Jakob Sch \.

Risk-aware Classification via Uncertainty Quantification u hl, and Jakob Sch \

Reference 23

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Observation cba9eb8b-e1ad-4123-89e3-e51bbb0673dd · outbound

This paper cites Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management.

Risk-aware Classification via Uncertainty Quantification Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management

Reference 24

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This paper cites Noise contrastive priors for functional uncertainty.

Risk-aware Classification via Uncertainty Quantification Noise contrastive priors for functional uncertainty

Reference 25

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Observation 1404908a-f65b-4266-8ead-f2bc7b132606 · outbound

This paper cites Aleatoric and Epistemic Uncertainty in Machine Learning : An Introduction to Concepts and Methods.

Risk-aware Classification via Uncertainty Quantification Aleatoric and Epistemic Uncertainty in Machine Learning : An Introduction to Concepts and Methods

Reference 26

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This paper cites Learning from bandit feedback: An overview of the state-of-the-art.

Risk-aware Classification via Uncertainty Quantification Learning from bandit feedback: An overview of the state-of-the-art

Reference 27

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Risk-aware Classification via Uncertainty Quantification Unresolved cited work

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Risk-aware Classification via Uncertainty Quantification Kingma and J

Reference 29

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Risk-aware Classification via Uncertainty Quantification Unresolved cited work

Reference 30

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Risk-aware Classification via Uncertainty Quantification Kendall and Y

Reference 31

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This paper cites What uncertainties do we need in bayesian deep learning for computer vision? In Advances in neural information processing systems , pages 5574--5584, 2017.

Risk-aware Classification via Uncertainty Quantification What uncertainties do we need in bayesian deep learning for computer vision? In Advances in neural information processing systems , pages 5574--5584, 2017

Reference 32

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This paper cites Cost-sensitive learning of deep feature representations from imbalanced data.

Risk-aware Classification via Uncertainty Quantification Cost-sensitive learning of deep feature representations from imbalanced data

Reference 33

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Risk-aware Classification via Uncertainty Quantification Cost-sensitive learning with neural networks

Reference 34

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Risk-aware Classification via Uncertainty Quantification Gradient-based learning applied to document recognition

Reference 35

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Risk-aware Classification via Uncertainty Quantification LeCun, P

Reference 36

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

Risk-aware Classification via Uncertainty Quantification Simple and principled uncertainty estimation with deterministic deep learning via distance awareness

Reference 37

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This paper cites Lakshminarayanan, A.

Risk-aware Classification via Uncertainty Quantification Lakshminarayanan, A

Reference 38

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Observation a6d329bc-8b93-4880-9e82-90dbe35381a2 · outbound

This paper cites Reliability analysis for finger movement recognition with raw electromyographic signal by evidential convolutional networks.

Risk-aware Classification via Uncertainty Quantification Reliability analysis for finger movement recognition with raw electromyographic signal by evidential convolutional networks

Reference 39

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Risk-aware Classification via Uncertainty Quantification Louizos and M

Reference 40

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Risk-aware Classification via Uncertainty Quantification Predictive uncertainty estimation via prior networks

Reference 41

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Risk-aware Classification via Uncertainty Quantification Reverse kl-divergence training of prior networks: Improved uncertainty and adversarial robustness

Reference 42

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Risk-aware Classification via Uncertainty Quantification Towards neural networks that provably know when they don't know

Reference 43

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Risk-aware Classification via Uncertainty Quantification Calibrating deep neural networks using focal loss

Reference 44

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This paper cites Deep Deterministic Uncertainty: A Simple Baseline.

Risk-aware Classification via Uncertainty Quantification Deep Deterministic Uncertainty: A Simple Baseline

Reference 45

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Risk-aware Classification via Uncertainty Quantification Machine learning: a probabilistic perspective

Reference 46

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This paper cites Deep Deterministic Uncertainty for Semantic Segmentation.

Risk-aware Classification via Uncertainty Quantification Deep Deterministic Uncertainty for Semantic Segmentation

Reference 47

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Risk-aware Classification via Uncertainty Quantification Pe 16-007

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:28.227440Z

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Observation 591900f5-13ee-495e-b328-08c1e73da91b · outbound

This paper cites Epistemic uncertainty quantification in deep learning classification by the delta method.

Risk-aware Classification via Uncertainty Quantification Epistemic uncertainty quantification in deep learning classification by the delta method

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:28.208967Z

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Observation f040fdc7-fdfb-40b7-9a46-905ab85a540b · outbound

This paper cites Implicit weight uncertainty in neural networks.

Risk-aware Classification via Uncertainty Quantification Implicit weight uncertainty in neural networks

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:28.191303Z

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Observation 5f095cc2-b45b-41be-a92c-45ce7376a6a9 · outbound

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Risk-aware Classification via Uncertainty Quantification Unresolved cited work

Reference 51

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This paper cites Mcua: Multi-level context and uncertainty aware dynamic deep ensemble for breast cancer histology image classification.

Risk-aware Classification via Uncertainty Quantification Mcua: Multi-level context and uncertainty aware dynamic deep ensemble for breast cancer histology image classification

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:28.157813Z

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Observation f60490d3-7384-4b00-b9c5-56d9c42d1547 · outbound

This paper cites Evidential deep learning for guided molecular property prediction and discovery.

Risk-aware Classification via Uncertainty Quantification Evidential deep learning for guided molecular property prediction and discovery

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:28.139879Z

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Observation 15298864-01ec-45f8-b546-2d0ce9f8b645 · outbound

This paper cites Reinforcement learning: An introduction.

Risk-aware Classification via Uncertainty Quantification Reinforcement learning: An introduction

Reference 54

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This paper cites Medspecsearch: Medical specialty search.

Risk-aware Classification via Uncertainty Quantification Medspecsearch: Medical specialty search

Reference 55

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Observation 7d42a4a3-916d-40a8-bd7b-f82cef1f4bec · outbound

This paper cites Leveraging evidential deep learning uncertainties with graph-based clustering to detect anomalies.

Risk-aware Classification via Uncertainty Quantification Leveraging evidential deep learning uncertainties with graph-based clustering to detect anomalies

Reference 56

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Observation 91b6ba3e-373e-4ef6-8c68-3e1423df1850 · outbound

This paper cites Uncertainty-aware deep classifiers using generative models.

Risk-aware Classification via Uncertainty Quantification Uncertainty-aware deep classifiers using generative models

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:28.077705Z

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Observation e4a70eef-df8f-48e8-9401-6ed812385b91 · outbound

This paper cites Evidential deep learning to quantify classification uncertainty.

Risk-aware Classification via Uncertainty Quantification Evidential deep learning to quantify classification uncertainty

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:28.061360Z

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Observation 55ff7e31-a9e9-447f-8da5-986de4ca9248 · outbound

This paper cites Evidential Deep Learning to Quantify Classification Uncertainty.

Risk-aware Classification via Uncertainty Quantification Evidential Deep Learning to Quantify Classification Uncertainty

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:28.045531Z

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Observation b65cf178-294b-4e6f-94d4-a9fe4e404074 · outbound

This paper cites Decision making in the TBM : The necessity of the pignistic transformation.

Risk-aware Classification via Uncertainty Quantification Decision making in the TBM : The necessity of the pignistic transformation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:28.028800Z

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Observation aca6aa02-9bcf-4f76-a3e3-08e0321782e4 · outbound

This paper cites Misclassification risk and uncertainty quantification in deep classifiers.

Risk-aware Classification via Uncertainty Quantification Misclassification risk and uncertainty quantification in deep classifiers

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:28.012588Z

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Observation 4ce6de3e-c6c8-4a0c-a007-9379b6ecefbb · outbound

This paper cites Functional variational bayesian neural networks.

Risk-aware Classification via Uncertainty Quantification Functional variational bayesian neural networks

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:27.993805Z

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Observation 86319e5e-298f-402b-9f30-4a269b87a3a0 · outbound

This paper cites Dusenberry, Du Phan, Mark Patrick Collier, Jie Jessie Ren, Kehang Han, Zi Wang, Zelda Mariet, Clara Huiyi Hu, Neil Band, Tim G.

Risk-aware Classification via Uncertainty Quantification Dusenberry, Du Phan, Mark Patrick Collier, Jie Jessie Ren, Kehang Han, Zi Wang, Zelda Mariet, Clara Huiyi Hu, Neil Band, Tim G

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:27.975038Z

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Observation 0423fd63-4cd7-446e-b8b1-4d189a1af69c · outbound

This paper cites Prior and posterior networks: A survey on evidential deep learning methods for uncertainty estimation.

Risk-aware Classification via Uncertainty Quantification Prior and posterior networks: A survey on evidential deep learning methods for uncertainty estimation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:27.958679Z

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Observation ea672589-7cf9-44b1-96c1-cd365f1d4d7f · outbound

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

Risk-aware Classification via Uncertainty Quantification Uncertainty estimation using a single deep deterministic neural network

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:27.941550Z

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Observation 4d47edae-b2a4-4349-8aba-7e59b930d723 · outbound

This paper cites Maximizing bci human feedback using active learning.

Risk-aware Classification via Uncertainty Quantification Maximizing bci human feedback using active learning

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:27.924442Z

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Observation ef419619-f289-472a-802d-f5507fb63282 · outbound

This paper cites Uncertainty estimation for stereo matching based on evidential deep learning.

Risk-aware Classification via Uncertainty Quantification Uncertainty estimation for stereo matching based on evidential deep learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:34:27.906705Z

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Pith citing papers

Observation ea9d714e-01d5-45ad-9ec0-176bb9874586 · inbound

Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification cites this paper.

Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification Risk-aware Classification via Uncertainty Quantification

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:15:52.149360Z

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