Paper Citation Record · LEDGER

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning

As of 22 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2602.08986.

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

pith.paper-citation-record.v1
2602.08986 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T03:09:24.373707Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

38 of 38 outbound references displayed

verified exact7
verified fuzzy0
unresolved27
parse uncertain0
malformed identifier4
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad2c96c0-0445-420c-a00d-133d300ec25e · outbound

This paper cites an unresolved cited work.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Unresolved cited work

Reference 3

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Observation e8c9e289-fad0-426f-af34-8fd582627127 · outbound

This paper cites Although we had expected a jump inF1 score due to increased precision, which was indeed observed, surprisingly, the recall too was boosted as a result of this combination.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Although we had expected a jump inF1 score due to increased precision, which was indeed observed, surprisingly, the recall too was boosted as a result of this combination

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T03:09:23.984198Z

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Observation 3e3c4eb8-daa0-4c75-982c-3e2962a09886 · outbound

This paper cites Zachary Daniels and Dimitris Metaxas.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Zachary Daniels and Dimitris Metaxas

Reference 9

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Observation e51f28c0-8685-4ae4-aefc-da6e30cd19e5 · outbound

This paper cites What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Reference 18

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Observation b5e8b7e3-b6f6-480d-97a9-3c8ada3705b6 · outbound

This paper cites doi: 10.1007/s11263-020-01316-z.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning doi: 10.1007/s11263-020-01316-z

Reference 20

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Observation 9875bc98-baea-40f6-b9d2-98cd6ba19999 · outbound

This paper cites Bin Liu, Konstantinos Blekas, and Grigorios Tsoumakas.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Bin Liu, Konstantinos Blekas, and Grigorios Tsoumakas

Reference 21

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Observation ca1a4f6f-23a4-4234-8f11-8bdd950acfde · outbound

This paper cites doi: https://doi.org/10.1016/j.patcog.2021.108294.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning doi: https://doi.org/10.1016/j.patcog.2021.108294

Reference 22

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Observation e57b6a5c-cc3b-4f06-b696-32c7bfeed3d1 · outbound

This paper cites doi: 10.1038/s41597-025-04491-1.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning doi: 10.1038/s41597-025-04491-1

Reference 23

Resolution: verified exact; doi, observed 2026-08-03T03:13:35.739333Z

Source-reported events for the cited work

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Observation b3ec5165-d505-4cc5-90dd-65320bd7c6d3 · outbound

This paper cites Understanding Model Calibration -- A gentle introduction and visual exploration of calibration and the expected calibration error (ECE).

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Understanding Model Calibration -- A gentle introduction and visual exploration of calibration and the expected calibration error (ECE)

Reference 24

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Observation 1bb27a29-0171-425a-8a8f-8dc74c3a67ee · outbound

This paper cites Anthony Robins.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Anthony Robins

Reference 26

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Observation 0b37a529-583c-4b40-b654-85f419d1a05d · outbound

This paper cites Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification

Reference 28

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Observation 95b9c44c-2457-4f1b-8aec-ee47e271f2a3 · outbound

This paper cites doi: 10.1093/nar/gkh894.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning doi: 10.1093/nar/gkh894

Reference 29

Resolution: verified exact; doi, observed 2026-08-03T03:13:35.099347Z

Source-reported events for the cited work

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Observation 3ae280ee-6835-43f9-a942-c9da5296ff14 · outbound

This paper cites Introducing an Improved Information-Theoretic Measure of Predictive Uncertainty.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Introducing an Improved Information-Theoretic Measure of Predictive Uncertainty

Reference 30

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Observation 2820595f-6217-499c-b053-8cec18eb41b2 · outbound

This paper cites doi: https: //doi.org/10.1016/j.patcog.2021.107965.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning doi: https: //doi.org/10.1016/j.patcog.2021.107965

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T03:09:23.113669Z

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Observation 87d31af9-29e5-4768-8e78-3ea4679f3934 · outbound

This paper cites Zhinuo Zhou, Ge-Yi Fu, Yi Fang, Ye Yuan, Hong-Bin Shen, Chun-Sheng Wang, Xue-Wei Xu, Peng Zhou, and Xiaoyong Pan.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Zhinuo Zhou, Ge-Yi Fu, Yi Fang, Ye Yuan, Hong-Bin Shen, Chun-Sheng Wang, Xue-Wei Xu, Peng Zhou, and Xiaoyong Pan

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T03:09:23.252816Z

Source-reported events for the cited work

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Observation b418d476-94ba-42a8-9919-9946fccbff64 · outbound

This paper cites doi: 10.3389/fmars.2023.1147690.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning doi: 10.3389/fmars.2023.1147690

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T03:09:23.387987Z

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Observation 184e01d6-d6f0-4bce-a4fb-d1a2c5b619ec · outbound

This paper cites For focal weighting experiments, we use aU0 = 0.25(matching our applied imbalance minimum gate˜w0), and a setk= 1— an empirically derived value in preliminary testing.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning For focal weighting experiments, we use aU0 = 0.25(matching our applied imbalance minimum gate˜w0), and a setk= 1— an empirically derived value in preliminary testing

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-03T03:09:23.550197Z

Source-reported events for the cited work

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Observation 46622452-4dd7-440a-aa98-af44e79d0b74 · outbound

This paper cites an unresolved cited work.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Unresolved cited work

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T03:09:24.106980Z

Source-reported events for the cited work

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Observation 933b1761-8435-43cd-bf48-ae92c6078483 · outbound

This paper cites No Weighting.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning No Weighting

Reference 39

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Observation 45cdee4d-c645-4fa3-8dd5-38df89c4ba3e · outbound

This paper cites The results foru0, andkwere attained with˜w0 set to0.25.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning The results foru0, andkwere attained with˜w0 set to0.25

Reference 40

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Observation 16a9b734-45b6-4c4a-bd01-d4a2a80bb1e8 · outbound

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Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Expr” and “Seq

Reference 128

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Observation 68c35f2d-5244-48ca-910f-c90c1199e0c1 · outbound

This paper cites URLhttps://doi.org/10.1080/09540099550039318.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning URLhttps://doi.org/10.1080/09540099550039318

Reference 1995

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Observation 1f489ea4-f68f-4734-89d1-287235969187 · outbound

This paper cites doi: 10.1038/75556.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning doi: 10.1038/75556

Reference 2000

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Observation f4473847-0250-4553-8da9-55f27eb8d0ae · outbound

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Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning ISBN 978-3-540-30115-8

Reference 2004

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Observation b6ce95f7-fa9f-4a3c-b3b4-b4c2be216fef · outbound

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Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Unresolved cited work

Reference 2006

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Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Last-layer committee machines for uncertainty estimations of benthic imagery

Reference 2011

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This paper cites URLhttps://doi.org/10.1016/j.seares.2012.04.009.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning URLhttps://doi.org/10.1016/j.seares.2012.04.009

Reference 2012

Resolution: verified exact; doi, observed 2026-08-03T03:13:36.621687Z

Source-reported events for the cited work

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Observation 3af49675-2133-41aa-94e5-c049c55dfacd · outbound

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Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning URLhttps://doi.org/10.1371/journal.pone.0141039

Reference 2015

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Observation 6aa5fb5d-429a-4467-8bee-7694d000f673 · outbound

This paper cites 14 Published in Transactions on Machine Learning Research (02/2026) E.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning 14 Published in Transactions on Machine Learning Research (02/2026) E

Reference 2016

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This paper cites URLhttps://ojs.aaai.org/index.php/AAAI/article/view/10908.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning URLhttps://ojs.aaai.org/index.php/AAAI/article/view/10908

Reference 2017

Resolution: verified exact; doi, observed 2026-08-03T03:13:36.340542Z

Source-reported events for the cited work

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Observation 76c31cca-d1ee-480a-9253-e904d9427974 · outbound

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Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Deep Learning using Rectified Linear Units (ReLU)

Reference 2018

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Observation 26933b47-9045-4453-9391-ed9ce343c9f0 · outbound

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Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Natural Adversarial Examples

Reference 2019

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Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning neurips.cc/paper_files/paper/2020/file/6dd4e10e3296fa63738371ec0d5df818-Paper.pdf

Reference 2020

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Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning doi: https://doi.org/10.1016/j.ins.2021.07.033

Reference 2021

Resolution: verified exact; doi, observed 2026-08-03T03:13:35.441797Z

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Observation f60e5cc3-8321-46eb-962f-f94705485714 · outbound

This paper cites doi: 10.1038/s41598-022-19939-2.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning doi: 10.1038/s41598-022-19939-2

Reference 2022

Resolution: verified exact; doi, observed 2026-08-03T03:13:36.041647Z

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Observation c609165c-8ba8-4f9a-8589-de8bb6ca9435 · outbound

This paper cites 16th Asian Conference on Computer Vision, ACCV 2022 ; Conference date: 04-12-2022 Through 08-12-2022.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning 16th Asian Conference on Computer Vision, ACCV 2022 ; Conference date: 04-12-2022 Through 08-12-2022

Reference 2023

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Observation 19fa8e8e-7736-447d-87f1-31011bd601fd · outbound

This paper cites Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning Hierarchical Classification for Automated Image Annotation of Coral Reef Benthic Structures

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-03T03:09:18.719270Z

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Observation e8e940ae-e2a7-4f76-8018-3f7d2f84113e · outbound

This paper cites ReefNet: A Large-Scale Dataset and Benchmark for Fine-Grained Coral Reef Recognition.

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning ReefNet: A Large-Scale Dataset and Benchmark for Fine-Grained Coral Reef Recognition

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T03:09:18.588487Z

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Pith citing papers

No inbound Pith citation observations are available.