Paper Citation Record · LEDGER

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity

As of 12 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2412.10942.

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

pith.paper-citation-record.v1
2412.10942 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:32:43.927790Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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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verified fuzzy19
unresolved14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8b47ebce-77e4-408b-adad-be87d1c85108 · outbound

This paper cites IEEE Access 6, 52138–52160 (2018).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity IEEE Access 6, 52138–52160 (2018)

Reference 1

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Observation c1cf4020-4c8e-4344-a07e-7e23c4801ed4 · outbound

This paper cites Advances in neural information processing systems 31 (2018).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Advances in neural information processing systems 31 (2018)

Reference 2

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Observation 3f391c8e-2a7a-44e9-9e75-a4a1bdf58ae7 · outbound

This paper cites Rethinking Stability for Attribution-based Explanations.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Rethinking Stability for Attribution-based Explanations

Reference 3

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Observation f27362a2-079c-47bc-ad2f-78dedce3ecca · outbound

This paper cites On the Robustness of Interpretability Methods.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity On the Robustness of Interpretability Methods

Reference 4

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Observation 36f85d1f-2dc7-40ba-85ee-65f2a0b25f88 · outbound

This paper cites Information Fusion 81, 14–40 (2022).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Information Fusion 81, 14–40 (2022)

Reference 5

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Observation f84f8be6-0565-413c-9bfd-23f7f677c5c7 · outbound

This paper cites PloS one 10(7), e0130140 (2015).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity PloS one 10(7), e0130140 (2015)

Reference 6

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Observation 470e4d1e-d57e-4859-b5bc-8ee85af94de9 · outbound

This paper cites Evaluating and Aggregating Feature-based Model Explanations.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Evaluating and Aggregating Feature-based Model Explanations

Reference 7

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Observation c8c14988-f8af-4dba-8f0c-245270132b67 · outbound

This paper cites Routledge (1984).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Routledge (1984)

Reference 8

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Observation c985b9fd-cf1b-420f-875d-787e51e1c45e · outbound

This paper cites Sensors 23(2), 634 (2023).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Sensors 23(2), 634 (2023)

Reference 9

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Observation 79759970-2405-447d-8e63-a28ed6fd5087 · outbound

This paper cites In: 2018 IEEE winter conference on applications of computer vision (W ACV).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity In: 2018 IEEE winter conference on applications of computer vision (W ACV)

Reference 10

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Observation f67b0dbe-587e-43fa-9393-526ecadf72a8 · outbound

This paper cites In: Proceedings of the IEEE Conf.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity In: Proceedings of the IEEE Conf

Reference 11

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Observation 2d4b21e7-a214-48bc-a1b6-02c729c388b6 · outbound

This paper cites Information Sciences 225, 1–17 (2013).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Information Sciences 225, 1–17 (2013)

Reference 12

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Observation d47fee60-3803-4c95-ba70-e7e1f2d559a8 · outbound

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Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Unresolved cited work

Reference 13

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Observation e070591f-d6c8-4263-bff8-7b55944f3170 · outbound

This paper cites Artificial Intelligence 291, 103428 (2021).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Artificial Intelligence 291, 103428 (2021)

Reference 14

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This paper cites Transactions on Machine Learning Research (2023).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Transactions on Machine Learning Research (2023)

Reference 15

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Observation 9a1a6ab6-6da5-419b-b467-b9fc869c211e · outbound

This paper cites Journal of Machine Learning Research 24(34), 1–11 (2023).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Journal of Machine Learning Research 24(34), 1–11 (2023)

Reference 16

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Observation 8e557083-177a-4fc1-a0a3-59922ca8f41b · outbound

This paper cites Metrics for Explainable AI: Challenges and Prospects.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Metrics for Explainable AI: Challenges and Prospects

Reference 17

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Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Unresolved cited work

Reference 18

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Observation e43b2839-1002-4504-9f4f-8a10f2919e21 · outbound

This paper cites The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

Reference 19

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Observation ed9db2d2-6b92-4e97-b863-3a88448512c5 · outbound

This paper cites Advances in neural information processing systems 25 (2012).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Advances in neural information processing systems 25 (2012)

Reference 20

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Observation d2f2a7e6-0e35-4fa9-98f6-b7626708e656 · outbound

This paper cites Artificial Intelligence for the Earth Systems 1(4), e220012 (2022).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Artificial Intelligence for the Earth Systems 1(4), e220012 (2022)

Reference 21

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Observation fdc6e106-bef0-418a-82c6-8fc872572c36 · outbound

This paper cites Artificial intelligence 267, 1–38 (2019).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Artificial intelligence 267, 1–38 (2019)

Reference 22

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Observation c896c0be-68c6-4f2d-98e2-3a06d20c7f39 · outbound

This paper cites Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations Datasets.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations Datasets

Reference 23

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This paper cites A novel approach to generate datasets with XAI ground truth to evaluate image models.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity A novel approach to generate datasets with XAI ground truth to evaluate image models

Reference 24

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Observation fd82595d-5595-4255-81f4-30857739d085 · outbound

This paper cites A comprehensive study on fidelity metrics for XAI.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity A comprehensive study on fidelity metrics for XAI

Reference 25

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This paper cites Applied Sciences 12(9), 4459 (2022).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Applied Sciences 12(9), 4459 (2022)

Reference 26

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This paper cites ACM Transactions on Interactive Intelligent Systems (TiiS)11(3-4), 1–45 (2021).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity ACM Transactions on Interactive Intelligent Systems (TiiS)11(3-4), 1–45 (2021)

Reference 27

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Observation acd248ac-80b3-4b5c-91fe-083951e344b6 · outbound

This paper cites Pattern recognition 127, 108604 (2022).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Pattern recognition 127, 108604 (2022)

Reference 28

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This paper cites Why should i trust you?.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Why should i trust you?

Reference 29

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Observation a983369c-7f41-4d56-8270-c6c5132bad6b · outbound

This paper cites IEEE transactions on neural networks and learning systems 28(11), 2660–2673 (2016).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity IEEE transactions on neural networks and learning systems 28(11), 2660–2673 (2016)

Reference 30

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Observation 34943acd-8efb-46ae-9915-c083ec7856a5 · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 31

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This paper cites In: Proceedings of the AAAI conference on artificial intelligence.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity In: Proceedings of the AAAI conference on artificial intelligence

Reference 32

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This paper cites Medical Image Analysis 79, 102470 (2022) 9 A PREPRINT - D ECEMBER 17, 2024.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Medical Image Analysis 79, 102470 (2022) 9 A PREPRINT - D ECEMBER 17, 2024

Reference 33

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This paper cites Advances in Neural Information Processing Systems 32 (2019).

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity Advances in Neural Information Processing Systems 32 (2019)

Reference 34

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This paper cites In: European conference on computer vision.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity In: European conference on computer vision

Reference 35

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Observation b53070a4-7f88-4421-ab41-49063b82d64c · outbound

This paper cites In: Proceedings of the IEEE conference on computer vision and pattern recognition.

Meta-evaluating stability measures: MAX-Senstivity & AVG-Sensitivity In: Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:32:44.402147Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.