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Paper Citation Record · LEDGER

Feature Attribution from First Principles

As of 21 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2505.24729.

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

pith.paper-citation-record.v1
2505.24729 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:26:21.207333Z

measured 42 of 42 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

42 of 42 outbound references displayed

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  • verified fuzzy37
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f59b222e-f2a1-4729-b37d-7555f0f26f34 · outbound

This paper cites Sanity Checks for Saliency Maps.

Feature Attribution from First Principles Sanity Checks for Saliency Maps

Reference 1

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Observation 416d1291-0dbe-4158-9af8-ff5e8a18e5b3 · outbound

This paper cites OpenXAI: Towards a Transparent Evaluation of Model Explanations.

Feature Attribution from First Principles OpenXAI: Towards a Transparent Evaluation of Model Explanations

Reference 2

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Observation 00deb67e-d5b4-4144-9fd8-4e35cd06b210 · outbound

This paper cites Functions of bounded variation, signed measures, and a general Koksma-Hlawka inequality.

Feature Attribution from First Principles Functions of bounded variation, signed measures, and a general Koksma-Hlawka inequality

Reference 3

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Observation 8d3c4ef7-1086-4644-8419-53006824dba0 · outbound

This paper cites Towards better understanding of gradient-based attribution methods for Deep Neural Networks.

Feature Attribution from First Principles Towards better understanding of gradient-based attribution methods for Deep Neural Networks

Reference 4

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Observation 1390bfc0-2707-4ba8-98fb-9665676b9272 · outbound

This paper cites On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation.

Feature Attribution from First Principles On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

Reference 5

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Observation fcf3250c-d34d-4e9b-a4cc-5b4b629b76e9 · outbound

This paper cites Are Artificial Neural Networks Black Boxes? IEEE Transactions on Neural Networks, 1997.

Feature Attribution from First Principles Are Artificial Neural Networks Black Boxes? IEEE Transactions on Neural Networks, 1997

Reference 6

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Observation 0774258e-0cc4-4553-bd6d-5d32c24e3ad6 · outbound

This paper cites How to safely discard features based on aggregate SHAP values.

Feature Attribution from First Principles How to safely discard features based on aggregate SHAP values

Reference 7

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Observation 7737d782-0e1b-4260-bd6a-1802a696a18f · outbound

This paper cites Impossibility Theorems for Feature Attribution.

Feature Attribution from First Principles Impossibility Theorems for Feature Attribution

Reference 8

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

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Observation e7be83fc-042d-4d2d-bea7-92297a92e6b5 · outbound

This paper cites Functions of bounded variation in one and multiple dimensions.

Feature Attribution from First Principles Functions of bounded variation in one and multiple dimensions

Reference 9

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

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Observation 9554821e-9e62-41c4-8f61-e6d9c2cec2ab · outbound

This paper cites A Theory of Interpretable Approximations.

Feature Attribution from First Principles A Theory of Interpretable Approximations

Reference 10

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Observation d43e4cf0-6692-4e58-b1ff-3daa721cc4c4 · outbound

This paper cites Introduction to Calculus and Analysis (volume II).

Feature Attribution from First Principles Introduction to Calculus and Analysis (volume II)

Reference 11

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Observation c791fe09-01e9-47d0-bc44-3f0e97db9fda · outbound

This paper cites Explaining by Removing: A Unified Framework for Model Explanation.

Feature Attribution from First Principles Explaining by Removing: A Unified Framework for Model Explanation

Reference 12

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Observation bf68396a-89d1-433d-a168-2fecc20932f5 · outbound

This paper cites Attribution-based Explanations that Provide Recourse Cannot be Robust.

Feature Attribution from First Principles Attribution-based Explanations that Provide Recourse Cannot be Robust

Reference 13

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Observation bc9ab381-8a61-487d-b5cb-b4204e4dc57c · outbound

This paper cites Real analysis: modern techniques and their applications.

Feature Attribution from First Principles Real analysis: modern techniques and their applications

Reference 14

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Source-reported events for the cited work

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Observation 0f797355-76a0-44d2-89c4-4ffc6f8ab9e8 · outbound

This paper cites Greedy Function Approximation: A Gradient Boosting Machine.

Feature Attribution from First Principles Greedy Function Approximation: A Gradient Boosting Machine

Reference 15

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

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Observation e315435d-d193-474b-9ae7-eeeb4c262b54 · outbound

This paper cites What does LIME really see in images? In International Conference on Machine Learning.

Feature Attribution from First Principles What does LIME really see in images? In International Conference on Machine Learning

Reference 16

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Observation b55d81d8-0064-44c3-9a61-b5bfed6d3165 · outbound

This paper cites Interpretation of Neural Networks is Fragile.

Feature Attribution from First Principles Interpretation of Neural Networks is Fragile

Reference 17

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Source-reported events for the cited work

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Observation a0d5933f-bdec-484d-86ff-23e7a06a74f8 · outbound

This paper cites New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound.

Feature Attribution from First Principles New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound

Reference 18

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Observation 1acce2d3-a822-42d3-8ea3-19cc3616c56f · outbound

This paper cites Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations.

Feature Attribution from First Principles Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations

Reference 19

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Source-reported events for the cited work

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Observation cfc24d80-9d09-4407-a1b8-d47df7e10c0b · outbound

This paper cites Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond.

Feature Attribution from First Principles Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond

Reference 20

Resolution
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Source-reported events for the cited work

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Observation 5d84d3b6-fc1f-4bcc-9e81-614d8e7cb15a · outbound

This paper cites Fooling Neural Network Interpretations via Adversarial Model Manipulation.

Feature Attribution from First Principles Fooling Neural Network Interpretations via Adversarial Model Manipulation

Reference 21

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Source-reported events for the cited work

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Observation a056e63a-24f4-4186-8a6a-423dab28175e · outbound

This paper cites Fast Axiomatic Attribution for Neural Networks.

Feature Attribution from First Principles Fast Axiomatic Attribution for Neural Networks

Reference 22

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Observation 1f03370d-7171-401c-b2f9-34461b5e2505 · outbound

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Feature Attribution from First Principles Unresolved cited work

Reference 23

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Observation 2c9eb3a5-d88b-468e-a4f2-37db8154da4e · outbound

This paper cites A Benchmark for Interpretability Methods in Deep Neural Networks.

Feature Attribution from First Principles A Benchmark for Interpretability Methods in Deep Neural Networks

Reference 24

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Source-reported events for the cited work

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Observation 3037aa5e-ba72-4277-b3da-03c3ab2a3534 · outbound

This paper cites SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries.

Feature Attribution from First Principles SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries

Reference 25

Resolution
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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.

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Observation 218acde2-08d9-4d67-a6f0-96013244aadc · outbound

This paper cites Concrete Representation of Abstract (M)-Spaces (A characterization of the Space of Continuous Functions).

Feature Attribution from First Principles Concrete Representation of Abstract (M)-Spaces (A characterization of the Space of Continuous Functions)

Reference 26

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Observation 7d0358ef-306b-4b62-895d-637c7a15b023 · outbound

This paper cites The (Un)reliability of saliency methods.

Feature Attribution from First Principles The (Un)reliability of saliency methods

Reference 27

Resolution
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Source-reported events for the cited work

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Observation 73351246-30e9-4e91-9c78-157dff0fd74e · outbound

This paper cites Disentangling Interactions and Dependencies in Feature Attribution.

Feature Attribution from First Principles Disentangling Interactions and Dependencies in Feature Attribution

Reference 28

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Source-reported events for the cited work

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Observation 2aa5d55d-be7f-420b-882e-1fde9c94a6b4 · outbound

This paper cites Attention Meets Post-hoc Inter- pretability: A Mathematical Perspective.

Feature Attribution from First Principles Attention Meets Post-hoc Inter- pretability: A Mathematical Perspective

Reference 29

Resolution
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Source-reported events for the cited work

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Observation 3e1ab81f-35a7-40b3-af0b-a5dc8025b7c4 · outbound

This paper cites A Unified Approach to Interpreting Model Predictions.

Feature Attribution from First Principles A Unified Approach to Interpreting Model Predictions

Reference 30

Resolution
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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.

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Observation 2c63c8c7-83a4-44f1-b5e8-750c55a6f7f6 · outbound

This paper cites Continuous Linear Representations.

Feature Attribution from First Principles Continuous Linear Representations

Reference 31

Resolution
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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.

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Observation b104630e-18f8-433d-abf4-f5bca213ecae · outbound

This paper cites Tangent Sets in the Space of Measures: With Applications to Variational Analysis.

Feature Attribution from First Principles Tangent Sets in the Space of Measures: With Applications to Variational Analysis

Reference 32

Resolution
verified fuzzy
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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.

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Observation e1984eb1-f3f2-4022-84fa-c64ece526c5a · outbound

This paper cites Steepest descent algorithms in a space of measures.Statistics and Computing, 2002.

Feature Attribution from First Principles Steepest descent algorithms in a space of measures.Statistics and Computing, 2002

Reference 33

Resolution
verified fuzzy
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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.

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Observation ae4bf3c0-00de-4ff7-93c0-25eb62be7974 · outbound

This paper cites Multidimensional Variation for Quasi-Monte Carlo.

Feature Attribution from First Principles Multidimensional Variation for Quasi-Monte Carlo

Reference 34

Resolution
verified fuzzy
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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.

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Observation 94659e82-1d55-4983-838b-e0ae20576d73 · outbound

This paper cites Mathematical theory of deep learning.

Feature Attribution from First Principles Mathematical theory of deep learning

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0094b8bd-9fd1-4ed1-8111-b924b38cf976 · outbound

This paper cites Why Should I Trust You?.

Feature Attribution from First Principles Why Should I Trust You?

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:26:23.462336Z

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.

source=pdf_text observed=2026-08-07T12:26:20.802419Z digest=sha256:7e57b1af60bf9dbdac996d993e8a9f89f985d732d5053b083203dff24d0cd01a

Observation 7a316bf8-6eb0-40cc-9bc2-cc54bfa0d0eb · outbound

This paper cites A Consistent and Efficient Evaluation Strategy for Attribution Methods.

Feature Attribution from First Principles A Consistent and Efficient Evaluation Strategy for Attribution Methods

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:26:23.183540Z

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.

source=pdf_text observed=2026-08-07T12:26:20.863745Z digest=sha256:ccdb15c701f725ffb0f42cc020cc087ae4c61c7acf1cb2f5a2205a93d72e958d

Observation 3a593242-087a-4c2d-8150-af8877a6cc65 · outbound

This paper cites Principles of Mathematical Analysis.

Feature Attribution from First Principles Principles of Mathematical Analysis

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:26:22.914199Z

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.

source=pdf_text observed=2026-08-07T12:26:20.926031Z digest=sha256:652f29334cccff5208384134f182891dcd384ded4af7e7aaca1ea23a6181f370

Observation d7aa6055-e003-4f85-89fa-3b22c19ccb62 · outbound

This paper cites Grad-CAM: Visual Explanations from Deep Networks via Gradient- based Localization.

Feature Attribution from First Principles Grad-CAM: Visual Explanations from Deep Networks via Gradient- based Localization

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:26:22.675227Z

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.

source=pdf_text observed=2026-08-07T12:26:20.981712Z digest=sha256:79f9e98a5ca3575f5160be7fd751be054d748333f80fae1bef27bf58d82ff7aa

Observation a7d127ef-ae1e-4be6-b51b-ad2132aece27 · outbound

This paper cites Learning Important Features Through Propagating Activation Differences.

Feature Attribution from First Principles Learning Important Features Through Propagating Activation Differences

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:26:22.428784Z

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.

source=pdf_text observed=2026-08-07T12:26:21.062257Z digest=sha256:dc8339b119438f4121b3dc431e1586bec8e007b7e517dccc7f26e638baf55673

Observation 682c8a9f-5c5d-4538-b7d5-1adf8b63c8a9 · outbound

This paper cites Axiomatic Attribution for Deep Networks.

Feature Attribution from First Principles Axiomatic Attribution for Deep Networks

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:26:22.123772Z

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.

source=pdf_text observed=2026-08-07T12:26:21.121007Z digest=sha256:3f02a0b5c036b92c5d7c002d9893d023d975bd44a6c4d47e8baf569831456bb7

Observation d5b70d82-9694-4a28-a304-65b5b3c8b20c · outbound

This paper cites #´ś p‰j ş r0,1s 1 dp1ypą0q ¯ş r0,1s yk dp2ykq if k“ j ,ş r0,1s yk dp1yką0q ş r0,1s 1 dp2yjqś p‰j,k ş r0,1s 1 dp1ypą0q else. (Fubini’s Theorem) “.

Feature Attribution from First Principles #´ś p‰j ş r0,1s 1 dp1ypą0q ¯ş r0,1s yk dp2ykq if k“ j ,ş r0,1s yk dp1yką0q ş r0,1s 1 dp2yjqś p‰j,k ş r0,1s 1 dp1ypą0q else. (Fubini’s Theorem) “

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:26:21.856867Z

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.

source=pdf_text observed=2026-08-07T12:26:21.207333Z digest=sha256:7dacd0cdf9b485210521ac0a06eee7abc6b6aee70fa514cff7c8d5d9669a8942

Pith citing papers

No inbound Pith citation observations are available.