Paper Citation Record · LEDGER

PXGen: A Post-hoc Explainable Method for Generative Models

As of 18 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2501.11827.

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

pith.paper-citation-record.v1
2501.11827 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:55:01.436257Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation a9bfd446-a2f7-441e-9158-67f089becfbf · outbound

This paper cites Machine learning36, 105–139 (1999).

PXGen: A Post-hoc Explainable Method for Generative Models Machine learning36, 105–139 (1999)

Reference 1

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Observation 5183d35c-7bfd-4826-a2a9-3ec175b81ea8 · outbound

This paper cites JAMA ophthalmology137(3), 258–264 (2019).

PXGen: A Post-hoc Explainable Method for Generative Models JAMA ophthalmology137(3), 258–264 (2019)

Reference 2

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Observation f5d288fd-7f18-48bb-afcf-294f1ad4f9e0 · outbound

This paper cites Max-sum diversity via convex programming.

PXGen: A Post-hoc Explainable Method for Generative Models Max-sum diversity via convex programming

Reference 3

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Observation 3d19dfc4-1817-46d9-8b91-c99c32905c6f · outbound

This paper cites In: Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms.

PXGen: A Post-hoc Explainable Method for Generative Models In: Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms

Reference 4

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PXGen: A Post-hoc Explainable Method for Generative Models Unresolved cited work

Reference 5

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Observation 2ff7dbec-3f06-475b-9a20-8faaad3e574b · outbound

This paper cites In: Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition.

PXGen: A Post-hoc Explainable Method for Generative Models In: Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition

Reference 6

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Observation c7a47973-fef8-42bf-906a-44be77e802c8 · outbound

This paper cites IEEE signal processing magazine29(6), 141–142 (2012).

PXGen: A Post-hoc Explainable Method for Generative Models IEEE signal processing magazine29(6), 141–142 (2012)

Reference 7

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Observation e229785f-4c15-4881-a807-9224b59541b5 · outbound

This paper cites Tutorial on Variational Autoencoders.

PXGen: A Post-hoc Explainable Method for Generative Models Tutorial on Variational Autoencoders

Reference 8

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Observation 0dbb5afa-8b25-46c6-bc8f-9a2b78d5f4c1 · outbound

This paper cites Adversarial Audio Synthesis.

PXGen: A Post-hoc Explainable Method for Generative Models Adversarial Audio Synthesis

Reference 9

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Observation f1096b96-5669-4225-a710-47be5873ee2d · outbound

This paper cites ACM Computing Surveys55(9), 1–33 (2023).

PXGen: A Post-hoc Explainable Method for Generative Models ACM Computing Surveys55(9), 1–33 (2023)

Reference 10

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Observation 4c1166a6-969e-4018-8895-f8cfdab9ae45 · outbound

This paper cites Advances In Neural Information Processing Systems35, 31841–31854 (2022).

PXGen: A Post-hoc Explainable Method for Generative Models Advances In Neural Information Processing Systems35, 31841–31854 (2022)

Reference 11

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Observation f1d226d5-d4cf-4a5d-8fed-be73290d2246 · outbound

This paper cites Journal of Artificial Intelligence Research 61, 65–170 (2018).

PXGen: A Post-hoc Explainable Method for Generative Models Journal of Artificial Intelligence Research 61, 65–170 (2018)

Reference 12

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Observation 185c660a-67fa-442d-9ea3-b2a9718d2db2 · outbound

This paper cites Advances in neural infor- mation processing systems27 (2014).

PXGen: A Post-hoc Explainable Method for Generative Models Advances in neural infor- mation processing systems27 (2014)

Reference 13

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Observation 1b14383a-1952-4a11-b590-15f160297750 · outbound

This paper cites Science robotics4(37), eaay7120 (2019).

PXGen: A Post-hoc Explainable Method for Generative Models Science robotics4(37), eaay7120 (2019)

Reference 14

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Observation f6bddb66-657c-410e-91f4-e6ac727d6ecc · outbound

This paper cites FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging.

PXGen: A Post-hoc Explainable Method for Generative Models FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging

Reference 15

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Observation c7164654-78d1-4d5e-9cdc-f9dd743980c0 · outbound

This paper cites Business Insider (2020).

PXGen: A Post-hoc Explainable Method for Generative Models Business Insider (2020)

Reference 16

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Observation 03820bc0-e461-4b94-90bf-2d2468e93b9a · outbound

This paper cites Training Data Influence Analysis and Estimation: A Survey.

PXGen: A Post-hoc Explainable Method for Generative Models Training Data Influence Analysis and Estimation: A Survey

Reference 17

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Observation f1752fe3-6aa4-4845-b7da-22fd72b40529 · outbound

This paper cites Operations research letters21(3), 133–137 (1997).

PXGen: A Post-hoc Explainable Method for Generative Models Operations research letters21(3), 133–137 (1997)

Reference 18

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Observation 17a45ccd-b603-43ee-9b42-b59d179b3a4d · outbound

This paper cites Advances in neural information processing systems30 (2017) 14 Huang et al.

PXGen: A Post-hoc Explainable Method for Generative Models Advances in neural information processing systems30 (2017) 14 Huang et al

Reference 19

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Observation 895a777b-0b46-4eec-9bb4-fae8ba413f0a · outbound

This paper cites ICLR (Poster)3 (2017).

PXGen: A Post-hoc Explainable Method for Generative Models ICLR (Poster)3 (2017)

Reference 20

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Observation ac2daaeb-dae2-4998-9418-634a1c20db11 · outbound

This paper cites Advances in neural information processing systems33, 6840–6851 (2020).

PXGen: A Post-hoc Explainable Method for Generative Models Advances in neural information processing systems33, 6840–6851 (2020)

Reference 21

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Observation 26178e2c-a6d5-4e82-a35a-c58866b568e7 · outbound

This paper cites Auto-Encoding Variational Bayes.

PXGen: A Post-hoc Explainable Method for Generative Models Auto-Encoding Variational Bayes

Reference 22

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Observation 5f22a4ef-f671-4f7c-a816-42fa8f914776 · outbound

This paper cites Advances in Neural Information Processing Systems 34, 2400–2412 (2021).

PXGen: A Post-hoc Explainable Method for Generative Models Advances in Neural Information Processing Systems 34, 2400–2412 (2021)

Reference 23

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Observation 23f76b82-c6dc-47fa-9552-3ab80d54505d · outbound

This paper cites The annals of mathe- matical statistics 22(1), 79–86 (1951).

PXGen: A Post-hoc Explainable Method for Generative Models The annals of mathe- matical statistics 22(1), 79–86 (1951)

Reference 24

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Observation 1ad312b2-d5c1-4e60-b5f5-168430dc4ec1 · outbound

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

PXGen: A Post-hoc Explainable Method for Generative Models Advances in Neural Information Processing Systems 32 (2019)

Reference 25

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Observation b53aa4dc-2a32-478a-b68b-9826b7be52f9 · outbound

This paper cites Advances in neural information processing systems30 (2017).

PXGen: A Post-hoc Explainable Method for Generative Models Advances in neural information processing systems30 (2017)

Reference 26

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Observation 84e3220a-3762-448c-a104-400004d8cd1e · outbound

This paper cites Understanding Diffusion Models: A Unified Perspective.

PXGen: A Post-hoc Explainable Method for Generative Models Understanding Diffusion Models: A Unified Perspective

Reference 27

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This paper cites Computational Linguistics pp.

PXGen: A Post-hoc Explainable Method for Generative Models Computational Linguistics pp

Reference 28

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Observation 21b50543-6a2a-4aed-b281-578cfe40ae1f · outbound

This paper cites Unpublished manuscript, March (2016).

PXGen: A Post-hoc Explainable Method for Generative Models Unpublished manuscript, March (2016)

Reference 29

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Observation 2a276911-2ca3-4e08-871b-05a36a493f00 · outbound

This paper cites Advances in Neural Information Processing Systems 33, 19920–19930 (2020).

PXGen: A Post-hoc Explainable Method for Generative Models Advances in Neural Information Processing Systems 33, 19920–19930 (2020)

Reference 30

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Observation 5eb4ac25-cc4c-4187-9c9a-9b8f466d79bb · outbound

This paper cites why should i trust you?.

PXGen: A Post-hoc Explainable Method for Generative Models why should i trust you?

Reference 31

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Observation 39f74a4f-08c3-4c80-90a1-0940096b64ed · outbound

This paper cites Personalized explanation in machine learning: A conceptualization.

PXGen: A Post-hoc Explainable Method for Generative Models Personalized explanation in machine learning: A conceptualization

Reference 32

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Observation 0ceebac2-40d3-4330-a2a1-4c9541ca5801 · outbound

This paper cites Explainable Generative AI (GenXAI): A Survey, Conceptualization, and Research Agenda.

PXGen: A Post-hoc Explainable Method for Generative Models Explainable Generative AI (GenXAI): A Survey, Conceptualization, and Research Agenda

Reference 33

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This paper cites In: Computer Graphics Forum.

PXGen: A Post-hoc Explainable Method for Generative Models In: Computer Graphics Forum

Reference 34

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Observation a9b62112-7a62-4f47-9da3-47f0293daaa4 · outbound

This paper cites In: International Conference on Machine Learning.

PXGen: A Post-hoc Explainable Method for Generative Models In: International Conference on Machine Learning

Reference 35

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

PXGen: A Post-hoc Explainable Method for Generative Models In: Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 36

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Observation 84f873c9-ce48-4fa9-b616-81cd70123d49 · outbound

This paper cites In: Proceedings of the twenty-fifth annual symposium on Computational geometry.

PXGen: A Post-hoc Explainable Method for Generative Models In: Proceedings of the twenty-fifth annual symposium on Computational geometry

Reference 37

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Observation 68c9f0e7-225e-40df-94bf-64517fee8e08 · outbound

This paper cites In: International conference on machine learning.

PXGen: A Post-hoc Explainable Method for Generative Models In: International conference on machine learning

Reference 38

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Observation 5d3cf49e-a913-41b5-b77b-522743295abf · outbound

This paper cites Nature biotechnology38(2), 143–145 (2020) PXGen: A Post-hoc Explainable Method for Generative Models 15.

PXGen: A Post-hoc Explainable Method for Generative Models Nature biotechnology38(2), 143–145 (2020) PXGen: A Post-hoc Explainable Method for Generative Models 15

Reference 39

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Observation 88e29437-8235-454a-a8df-492d3a654c52 · outbound

This paper cites IEEE transactions on image processing 13(4), 600–612 (2004).

PXGen: A Post-hoc Explainable Method for Generative Models IEEE transactions on image processing 13(4), 600–612 (2004)

Reference 40

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

PXGen: A Post-hoc Explainable Method for Generative Models Advances in neural information processing systems 31 (2018)

Reference 41

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This paper cites Interpreting Language Models with Contrastive Explanations.

PXGen: A Post-hoc Explainable Method for Generative Models Interpreting Language Models with Contrastive Explanations

Reference 42

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Observation f314f8a0-ff1b-4daf-bdf1-86a6f61ffa2a · outbound

This paper cites In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency.

PXGen: A Post-hoc Explainable Method for Generative Models In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T17:55:01.623271Z

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Pith citing papers

No inbound Pith citation observations are available.