Paper Citation Record · LEDGER

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms

As of 21 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2412.04166.

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

pith.paper-citation-record.v1
2412.04166 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:49:28.139930Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

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

36 of 36 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 468502ee-bc9b-49cc-a5a3-554ebaad8d04 · outbound

This paper cites Image pre- processing in computer vision systems for melanoma detection.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Image pre- processing in computer vision systems for melanoma detection

Reference 1

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Observation 8710c8cf-0e6c-49fb-9794-8289f9c44cc9 · outbound

This paper cites Computer aided melanoma skin cancer detection using image processing.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Computer aided melanoma skin cancer detection using image processing

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.699681Z

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Observation a11f731f-4315-40e3-9495-b93ee27f7ef1 · outbound

This paper cites Computer vision and digital imaging technology in melanoma detection.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Computer vision and digital imaging technology in melanoma detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.682331Z

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Observation b7780b91-ec39-491b-a5c5-69c75c0ccaf6 · outbound

This paper cites Scalable systems for early fault detection in wind turbines: a data driven approach.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Scalable systems for early fault detection in wind turbines: a data driven approach

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.667229Z

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Observation a7761c70-b982-4959-ae25-90af5fd3f3e9 · outbound

This paper cites Deep learning for automated drivetrain fault detection.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Deep learning for automated drivetrain fault detection

Reference 5

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Observation 8dd43f8e-5506-4c55-9af9-14ffc5d9d100 · outbound

This paper cites Weinberger.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Weinberger

Reference 6

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Observation 828cd068-fff8-4f95-8464-80c2ef492253 · outbound

This paper cites Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.619135Z

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Observation 33641f42-3c92-4b72-8eff-86e9006cd556 · outbound

This paper cites Transforming classifier scores into accurate multiclass probability estimates.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Transforming classifier scores into accurate multiclass probability estimates

Reference 8

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Observation cc3bf84b-cad3-423a-afea-fe8f12ec9a1b · outbound

This paper cites Cooper, and Milos Hauskrecht.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Cooper, and Milos Hauskrecht

Reference 9

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Observation 143ced48-af71-414b-b18f-1c3d867c67b6 · outbound

This paper cites A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

Reference 10

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Source-reported events for the cited work

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Observation d8ec4122-d956-4ede-a8b9-3b340f50155c · outbound

This paper cites Algorithmic learning in a random world , volume 29.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Algorithmic learning in a random world , volume 29

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.577620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7d043147-ae86-4e6f-ab9e-8bd3206e1844 · outbound

This paper cites Conformalized quantile regression.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Conformalized quantile regression

Reference 12

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Observation 72ce85ab-5395-44a3-b73a-743a272d2ab5 · outbound

This paper cites Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Image-to-Image Regression with Distribution-Free Uncertainty Quantification and Applications in Imaging

Reference 13

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Observation d619919e-154a-4268-8d1d-a0a1efe98cf8 · outbound

This paper cites Angelopoulos, Jennifer Listgarten, and Michael I.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Angelopoulos, Jennifer Listgarten, and Michael I

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.552017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 683da361-1ffb-4509-9a56-d79b97bb7a68 · outbound

This paper cites Semantic uncertainty intervals for disentangled latent spaces.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Semantic uncertainty intervals for disentangled latent spaces

Reference 15

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Observation 37e4c822-99ac-4c41-8a28-cdba47415ced · outbound

This paper cites Uncertainty Sets for Image Classifiers using Conformal Prediction.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Uncertainty Sets for Image Classifiers using Conformal Prediction

Reference 16

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Observation 343053d1-bd03-456e-89de-4aa8d94aa41f · outbound

This paper cites Con- formal prediction sets for ordinal classification.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Con- formal prediction sets for ordinal classification

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.524446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4b7abf46-18c6-4847-bb6e-350d3e373c92 · outbound

This paper cites Improving expert predictions with conformal prediction.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Improving expert predictions with conformal prediction

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.507804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b61fd8c2-276b-4d08-82ae-5217e024c969 · outbound

This paper cites Least ambiguous set-valued classifiers with bounded error levels.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Least ambiguous set-valued classifiers with bounded error levels

Reference 20

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Observation 12706e4e-2789-4654-8ac6-36ad6f2d2719 · outbound

This paper cites Jaws: Auditing predictive uncertainty under covariate shift.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Jaws: Auditing predictive uncertainty under covariate shift

Reference 21

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Observation ed76afdb-e6d9-430c-a0f3-c567141930fe · outbound

This paper cites Distribution-free risk assessment of regression-based machine learning algorithms, 2023.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Distribution-free risk assessment of regression-based machine learning algorithms, 2023

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.466024Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e9f65240-d24b-4f4e-bb3d-3823ef33894f · outbound

This paper cites Classifier calibration: a survey on how to assess and improve predicted class probabilities.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Classifier calibration: a survey on how to assess and improve predicted class probabilities

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.448974Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c517c539-7913-4a1c-b591-ba7aedbbd89b · outbound

This paper cites Measuring calibration in deep learning.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Measuring calibration in deep learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.429116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 45969c37-2198-4211-9ac4-418931fa52f7 · outbound

This paper cites Inductive confidence machines for regression.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Inductive confidence machines for regression

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.412134Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 98da52be-6651-40bd-bdfe-090801dbb70c · outbound

This paper cites Co- variate shift adaptation by importance weighted cross validation.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Co- variate shift adaptation by importance weighted cross validation

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 15b1ed5a-6209-4452-aac5-aa3167067025 · outbound

This paper cites Normalized nonconformity measures for regression conformal prediction.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Normalized nonconformity measures for regression conformal prediction

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.379512Z

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Observation 03b70e9e-fe21-4472-8ab5-521e3a3028cf · outbound

This paper cites Classification with valid and adaptive coverage.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Classification with valid and adaptive coverage

Reference 28

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Observation c3c8c01b-8984-4ea5-8ec2-cb0401cf6683 · outbound

This paper cites Cifar-100 dataset.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Cifar-100 dataset

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.363614Z

Source-reported events for the cited work

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Observation 01e7685a-c091-4039-bbe4-c411d4825279 · outbound

This paper cites Automated flower classification over a large number of classes.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Automated flower classification over a large number of classes

Reference 30

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Observation b2422e79-6e36-4e70-bfbd-9cc637e48a8c · outbound

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

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Imagenet: A large-scale hierarchical image database

Reference 31

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Observation fc1b5250-d9bb-4657-9da4-25cae56551bc · outbound

This paper cites Places: A 10 million image database for scene recognition.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Places: A 10 million image database for scene recognition

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.320008Z

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Observation 08f7a7d5-5e72-45d6-b3e7-e7c841b3377e · outbound

This paper cites Deep residual learning for image recognition.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Deep residual learning for image recognition

Reference 33

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Observation d7d7a60b-eeb5-4797-a560-5b4b86707500 · outbound

This paper cites Densely connected convolutional networks.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Densely connected convolutional networks

Reference 34

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Source-reported events for the cited work

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Observation e98ea915-12dc-4d25-9fff-4ee259b2a1d8 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Imagenet classification with deep convolutional neural networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.282032Z

Source-reported events for the cited work

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Observation 75164212-9631-4c62-8c88-560d5f7e345e · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 36

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Source-reported events for the cited work

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Observation dca8ddae-1420-400d-9150-b050ef89ea88 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

An In-Depth Examination of Risk Assessment in Multi-Class Classification Algorithms Pytorch: An imperative style, high-performance deep learning library

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:49:28.264742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.