Paper Citation Record · LEDGER

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI

As of 9 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 1 inbound Pith citation observation for arXiv:2510.01038.

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

pith.paper-citation-record.v1
2510.01038 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:15:26.302918Z

measured 53 of 53 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-13T18:53:48.881039Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T18:58:08.963428Z

Reference resolution

52 of 52 outbound references displayed

verified exact0
verified fuzzy0
unresolved51
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malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7f2b35e8-f20a-4e72-8836-6748c03a463c · outbound

This paper cites On pixel-wise ex- planations for non-linear classifier decisions by layer-wise relevance propa- gation.PLoS ONE, 10(7), 2015.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI On pixel-wise ex- planations for non-linear classifier decisions by layer-wise relevance propa- gation.PLoS ONE, 10(7), 2015

Reference 1

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Observation 0fab1713-8324-49fd-9615-ac231c3e9be3 · outbound

This paper cites an unresolved cited work.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:23.313227Z

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Observation e713d5d1-ae3f-4315-8212-40fcee603199 · outbound

This paper cites SpecReX: Explainable AI for Raman Spectroscopy.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI SpecReX: Explainable AI for Raman Spectroscopy

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:23.387202Z

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Observation 2f37dc30-e209-401c-b9cf-1ba66c9c60b1 · outbound

This paper cites MeLIME: Meaningful Local Explanation for Machine Learning Models.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI MeLIME: Meaningful Local Explanation for Machine Learning Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:23.453473Z

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Observation b4f6189c-5934-4b54-b25f-68ecef73889c · outbound

This paper cites an unresolved cited work.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Unresolved cited work

Reference 5

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Observation 3dab95a3-5ced-4dd4-9c28-beaed3f10ce8 · outbound

This paper cites Grad-cam++: Generalized gradient-based visual expla- nations for deep convolutional networks.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Grad-cam++: Generalized gradient-based visual expla- nations for deep convolutional networks

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:23.575145Z

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Observation 85b465c2-195a-48e9-9f57-b371d99eec57 · outbound

This paper cites an unresolved cited work.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Unresolved cited work

Reference 7

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Observation cb8b53dd-c7ee-4afd-bbbf-a5cdc2216f7b · outbound

This paper cites an unresolved cited work.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:23.766342Z

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Observation eb9cbff6-6516-4542-8237-de17dfe0d4c1 · outbound

This paper cites Causal Explanations for Image Classifiers.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Causal Explanations for Image Classifiers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:23.830181Z

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Observation 00c84f40-e9f3-42c1-9c56-59abe8afecb7 · outbound

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

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Ima- genet: A large-scale hierarchical image database

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:23.900745Z

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Observation 3a70f8c3-feef-4523-a94a-7aee668c3a2a · outbound

This paper cites Explanations can be manipulated and geometry is to blame.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Explanations can be manipulated and geometry is to blame

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:23.975544Z

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Observation f4eb86de-0071-4c3b-a4b7-178ba8a9a01a · outbound

This paper cites Williams, John Winn, and Andrew Zisserman.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Williams, John Winn, and Andrew Zisserman

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:24.062926Z

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Observation a85655cd-206e-4031-996b-318ed81145fb · outbound

This paper cites MIT Press, 1988.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI MIT Press, 1988

Reference 13

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Observation d675809c-fde3-4025-a0d3-16eb4547528d · outbound

This paper cites Glymour and F.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Glymour and F

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:24.207544Z

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Observation 6bdc5b12-4785-4fe1-b7ec-53c0a1294c64 · outbound

This paper cites Caltech 256, Apr 2022.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Caltech 256, Apr 2022

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:24.271514Z

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Observation 40f0c8e8-c448-4b72-8518-0994b7499133 · outbound

This paper cites an unresolved cited work.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Unresolved cited work

Reference 16

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Observation 5322bef5-7c4b-4c8f-95db-2d1ae2cc6b0e · outbound

This paper cites an unresolved cited work.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Unresolved cited work

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:24.439922Z

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Observation 6d666a09-dc3c-4f43-9a98-ff3ad08d748d · outbound

This paper cites Halpern.Actual Causality.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Halpern.Actual Causality

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:24.518045Z

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Observation 345b7abf-d86b-47ee-a6fe-8878c9250ee1 · outbound

This paper cites audioLIME: Listenable Explanations Using Source Separation.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI audioLIME: Listenable Explanations Using Source Separation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:24.605395Z

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Observation 1ff1aefc-ae31-4a86-a27f-8738fb9281d2 · outbound

This paper cites Deep residual learning for image recognition.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Deep residual learning for image recognition.2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778, 2016

Reference 20

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Observation 5488e720-2a7c-4a5c-89e6-3a98b0dee3d7 · outbound

This paper cites Free Press, 1965.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Free Press, 1965

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:24.738200Z

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Observation 1b209917-b75a-4d56-bd08-54368f1c0c0a · outbound

This paper cites Hitchcock.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Hitchcock

Reference 22

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Observation 39b2a69c-9c01-4e15-bfff-dba8b9b3a098 · outbound

This paper cites Hitchcock.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Hitchcock

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:24.837254Z

Source-reported events for the cited work

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Observation ac732c08-5de5-4cdf-9d46-c177f3489782 · outbound

This paper cites A benchmark for interpretability methods in deep neural networks.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI A benchmark for interpretability methods in deep neural networks

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:24.915162Z

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Observation 3689b086-c49d-41e0-81fb-d4189297970e · outbound

This paper cites I Am Big, You Are Little; I Am Right, You Are Wrong.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI I Am Big, You Are Little; I Am Right, You Are Wrong

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:25.033682Z

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Observation 8761655b-c27f-4e45-b1a6-77502f8f461e · outbound

This paper cites Causal identification of sufficient, con- trastive and complete feature sets in image classification.arXiv preprint arXiv:2507.23497, 2025.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Causal identification of sufficient, con- trastive and complete feature sets in image classification.arXiv preprint arXiv:2507.23497, 2025

Reference 26

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Observation fc99263a-e245-4f3c-b888-ba44b7dc22fb · outbound

This paper cites Lundberg and Su-In Lee.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Lundberg and Su-In Lee

Reference 27

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Observation b5a9457b-063e-497f-ba33-9b2c536ff139 · outbound

This paper cites Segal time series clas- sification—stable explanations using a generative model and an adaptive weighting method for lime.Neural Networks, 176:106345, 2024.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Segal time series clas- sification—stable explanations using a generative model and an adaptive weighting method for lime.Neural Networks, 176:106345, 2024

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:25.427904Z

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Observation 04b525a1-e45b-4f00-bca9-de886b24adb2 · outbound

This paper cites Local interpretable model-agnostic explanations for music content analysis.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Local interpretable model-agnostic explanations for music content analysis

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:25.544816Z

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Observation b77212ef-15c2-4519-aa3e-0aa017f798b9 · outbound

This paper cites Morgan Kauf- mann, 1988.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Morgan Kauf- mann, 1988

Reference 30

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Observation 85ad1cc8-54bb-46e3-8313-7ad8008fd671 · outbound

This paper cites RISE: randomized input sam- pling for explanation of black-box models.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI RISE: randomized input sam- pling for explanation of black-box models

Reference 31

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Observation cf7389e2-02e9-4a30-9234-2c2456d13b71 · outbound

This paper cites Designing network design spaces.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Designing network design spaces

Reference 32

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Observation 2b8ec4d4-8863-40b4-ab01-b70d71b39033 · outbound

This paper cites Do imagenet classifiers generalize to imagenet? InInternational conference on machine learning, pages 5389–5400.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Do imagenet classifiers generalize to imagenet? InInternational conference on machine learning, pages 5389–5400

Reference 33

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Observation 913fe0ed-6d16-4430-b4fa-e2a186dda468 · outbound

This paper cites Why should I trust you?.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Why should I trust you?

Reference 34

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Observation 851f0949-74f9-4a13-8a10-1f9142d94c5f · outbound

This paper cites Anchors: high-precision model-agnostic explanations.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Anchors: high-precision model-agnostic explanations

Reference 35

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Observation 24cbab49-5e26-442c-954e-633929e5a4a1 · outbound

This paper cites Salmon.Four Decades of Scientific Explanation.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Salmon.Four Decades of Scientific Explanation

Reference 36

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Observation 2e8efd02-9a82-4b99-9bc2-8d688a1fc5fa · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Grad-cam: Visual explanations from deep networks via gradient-based localization

Reference 37

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Observation c92b6a40-63e9-4250-b88f-6acca32dc39f · outbound

This paper cites Learning im- portant features through propagating activation differences.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Learning im- portant features through propagating activation differences

Reference 38

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Observation a331fae7-7851-4b44-812c-ab71d196c15f · outbound

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

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 39

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Observation 45c61852-cc8f-4320-9149-3db42ed41c5d · outbound

This paper cites Limesegment: Meaningful, realistic time series explanations.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Limesegment: Meaningful, realistic time series explanations

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:26.253433Z

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Observation b8cda41c-d9c3-4e60-bac1-c63c36b41cc5 · outbound

This paper cites Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:26.257423Z

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Observation 17d99c2d-7d13-4c8b-9b09-b0327aa4e86f · outbound

This paper cites Riedmiller.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Riedmiller

Reference 42

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Observation 1c7d7155-502b-4df8-96c5-d440233fd133 · outbound

This paper cites Axiomatic attribution for deep networks.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Axiomatic attribution for deep networks

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-04T13:15:26.265867Z

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Observation 3a3fd05a-4071-435d-9cc6-f0d6d0a981a8 · outbound

This paper cites Efficientnetv2: Smaller models and faster training.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Efficientnetv2: Smaller models and faster training

Reference 44

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Observation 1635a3ef-310f-461a-97e9-cbcc1b80bf69 · outbound

This paper cites When can you trust your explanations? a robustness analysis on feature importances.arXiv preprint arXiv:2406.14349, 2024.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI When can you trust your explanations? a robustness analysis on feature importances.arXiv preprint arXiv:2406.14349, 2024

Reference 45

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Observation 62ccb53e-2b28-44df-affc-6679a78f9a8a · outbound

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Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Unresolved cited work

Reference 46

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Observation b1b668f4-49e3-4d12-823a-a4c9e14106ee · outbound

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Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Woodward.Making Things Happen: A Theory of Causal Explanation

Reference 47

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Observation fbc9bd41-b26e-4c05-8f4a-f1f222ca9af5 · outbound

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Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Ml-loo: Detecting adversarial examples with feature attribution

Reference 48

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Observation 9ad394a0-a539-4c1d-9d2f-3af15e94dd11 · outbound

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Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Unresolved cited work

Reference 49

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Observation 9c47b678-8789-4266-bf7b-d11a761258ed · outbound

This paper cites Visualizing and understanding con- volutional networks.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Visualizing and understanding con- volutional networks

Reference 50

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Observation 50a5c7fe-5cdc-4d7d-ab71-a90deef75562 · outbound

This paper cites Baylime: Bayesian local interpretable model-agnostic explanations.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI Baylime: Bayesian local interpretable model-agnostic explanations

Reference 51

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Observation a056bfe2-d747-411c-af0d-3f7b13ee385f · outbound

This paper cites F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI.

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI

Reference 52

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Pith citing papers

Observation 4eb501fc-c64a-48a4-8d51-5f9a0658df74 · inbound

If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models cites this paper.

If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-07T03:17:12.509592Z

Source-reported events for the cited work

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