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Paper Citation Record · LEDGER

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning

As of 14 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 2026-08-14T06:32:32.682623+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

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External citation measurements

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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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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

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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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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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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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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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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

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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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This paper cites Anthony Robins.

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

Reference 26

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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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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

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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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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

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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

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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

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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

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

Reference 38

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

Reference 39

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

Reference 128

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

Reference 2000

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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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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

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

Reference 2017

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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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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

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

Reference 2022

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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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T03:09:20.536937Z digest=sha256:6ec51b7d3e88c5dd5e3ddcd1122044b9745ceafbc983340b3699e01e35569a84

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

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source=pdf_text observed=2026-08-03T03:09:18.719270Z digest=sha256:fd95ca6a9bb16f4abab6282f84c03e6bddf137ff33c0db86157576ea685aa01e

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

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source=pdf_text observed=2026-08-03T03:09:18.588487Z digest=sha256:b2c52acd94e72929c65135bf94fd071a836820c5a94c812e356b6e16349d2246

Pith citing papers

No inbound Pith citation observations are available.