Paper Citation Record · LEDGER

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2607.17783.

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

pith.paper-citation-record.v1
2607.17783 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:39:20.499420Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

32 of 32 outbound references displayed

verified exact5
verified fuzzy12
unresolved10
parse uncertain0
malformed identifier3
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 577dd2cc-5e89-4e9d-940d-27229bc8f9c7 · outbound

This paper cites Artificial Intelli- gence298(2021).https://doi.org/10.1016/j.artint.2021.103502.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Artificial Intelli- gence298(2021).https://doi.org/10.1016/j.artint.2021.103502

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T15:39:20.367979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99e8c103-3546-4cce-aa8e-85c54ff2f724 · outbound

This paper cites In: Proceedings of the 36th International Conference on Neural In- formation Processing Systems.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: Proceedings of the 36th International Conference on Neural In- formation Processing Systems

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:39:21.298654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 74fefaf3-ebb2-4baf-959a-0c73a4f9897b · outbound

This paper cites In: XAI World Conference (2025).

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: XAI World Conference (2025)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:39:21.285876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dfbb323c-bc8d-40a5-abf8-7ff251a2ddf3 · outbound

This paper cites In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T15:39:20.380516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7b0e512-0a2a-4e46-b5ea-3724ac8eed09 · outbound

This paper cites Data Mining and Knowledge Discov- ery (2025).https://doi.org/10.1007/s10618-025-01117-3.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Data Mining and Knowledge Discov- ery (2025).https://doi.org/10.1007/s10618-025-01117-3

Reference 5

Resolution: verified exact; doi, observed 2026-08-15T15:39:20.655772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0e6c423e-0b5f-4ddd-839e-fc925e8c597c · outbound

This paper cites Explaining Time Series Predictions with Dynamic Masks.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Explaining Time Series Predictions with Dynamic Masks

Reference 6

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T15:39:20.644147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 22f12231-0c3d-4492-8ba9-a87bda6aa2e5 · outbound

This paper cites In: BPM (2025).https://doi.org/ 10.1007/978-3-032-02929-4_10.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: BPM (2025).https://doi.org/ 10.1007/978-3-032-02929-4_10

Reference 7

Resolution: verified exact; doi, observed 2026-08-15T15:39:20.629050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 57104849-6921-4d22-a03e-f3fac50f88cc · outbound

This paper cites Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies4(3) (2020).https://doi.org/10.1145/3411832.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies4(3) (2020).https://doi.org/10.1145/3411832

Reference 8

Resolution: verified exact; doi, observed 2026-08-15T15:39:20.616836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fdf9623b-ec1c-416e-a240-d797901ed3b7 · outbound

This paper cites In: ICDM (2022).

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: ICDM (2022)

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:39:21.273227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 77ab127b-6b10-413a-ba25-21c561cad2dd · outbound

This paper cites Algorithms (2022).

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Algorithms (2022)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:39:21.260349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9a14e907-f573-4993-bbfb-dd2b7181c2a3 · outbound

This paper cites In: ICPM (2020).

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: ICPM (2020)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:39:21.247760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dbf2a2b5-4885-4c99-8c65-d3107d0f4358 · outbound

This paper cites Cell Reports Physical Science (2026).https://doi.org/10.1016/j.xcrp.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Cell Reports Physical Science (2026).https://doi.org/10.1016/j.xcrp

Reference 12

Resolution: malformed identifier; no resolver link, observed 2026-08-15T15:39:20.416343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd0938da-ce81-4934-822e-5d61f9334d46 · outbound

This paper cites In: DAS- FAA (2024).

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: DAS- FAA (2024)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:39:21.235377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 25484147-e19c-4767-8584-de3dce3021b9 · outbound

This paper cites Journal of the American Statistical Association 107(500) (2012).https://doi.org/10.1080/01621459.2012.737745 Feature Attribution-Based Explainability Analysis in PPM 17.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Journal of the American Statistical Association 107(500) (2012).https://doi.org/10.1080/01621459.2012.737745 Feature Attribution-Based Explainability Analysis in PPM 17

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T15:39:20.424964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d5b2247-ab8e-4df8-97a6-60eb039db17d · outbound

This paper cites Journal of the Royal Statistical Society Series B: Statistical Methodology86(2) (2024).

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Journal of the Royal Statistical Society Series B: Statistical Methodology86(2) (2024)

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:39:21.221578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cbda5746-87f1-49d0-a7cf-121ef66a7bfd · outbound

This paper cites In: ICLR (2024).

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: ICLR (2024)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:39:21.206649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 048cf547-ea3c-4280-b6a3-a677c491ed96 · outbound

This paper cites A Unified Approach to Interpreting Model Predictions.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring A Unified Approach to Interpreting Model Predictions

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T15:39:20.437904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 551cee2f-353f-4496-9e43-30542b187d1a · outbound

This paper cites Journal of Biomedical Informatics144, 104438 (2023).

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Journal of Biomedical Informatics144, 104438 (2023)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:39:21.190910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8e2e6361-a5e8-4aab-9380-34998ff4c9fe · outbound

This paper cites Information Systems139(2026).https://doi.org/10.1016/j.is.2026.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Information Systems139(2026).https://doi.org/10.1016/j.is.2026

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T15:39:20.446478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c74f397d-3ad1-4dc7-a42b-30a39e60a5dd · outbound

This paper cites In: ICPM (2020).https: //doi.org/10.1109/ICPM49681.2020.00024.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: ICPM (2020).https: //doi.org/10.1109/ICPM49681.2020.00024

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T15:39:20.450784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d733039f-5ae0-402e-9327-5ea3435493d8 · outbound

This paper cites In: BPM (2020).https: //doi.org/10.1007/978-3-030-58638-6_9.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: BPM (2020).https: //doi.org/10.1007/978-3-030-58638-6_9

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-15T15:39:20.454611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67927a28-16a1-4b9d-8093-e900db005d9a · outbound

This paper cites In: ECML-PKDD (2021).https://doi.org/10.1007/978-3-030-93736-2_40.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: ECML-PKDD (2021).https://doi.org/10.1007/978-3-030-93736-2_40

Reference 22

Resolution: verified exact; doi, observed 2026-08-15T15:39:20.579391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0f69ee7e-a2c8-4cfe-bce0-ea02eaa24a1c · outbound

This paper cites The Bell system tech- nical journal27(3) (1948).

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring The Bell system tech- nical journal27(3) (1948)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:39:21.177926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 02fcc432-94ea-4e89-adbf-6cefc2736cea · outbound

This paper cites In: International Conference on Artificial Intelligence and Statistics (2022).

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: International Conference on Artificial Intelligence and Statistics (2022)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:39:21.163191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 497b881f-88ff-45b0-a185-e41a5b36fd63 · outbound

This paper cites IEEE TSC (2025).https://doi.org/10.1109/TSC.2025.3609837.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring IEEE TSC (2025).https://doi.org/10.1109/TSC.2025.3609837

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T15:39:20.470738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8bc6c21-b402-47a3-a964-c9a47cd3a51a · outbound

This paper cites Operator thermalization vs eigenstate thermalization.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Operator thermalization vs eigenstate thermalization

Reference 26

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T15:39:20.567633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6f18b881-5c5d-4192-920d-a37c78bca0f7 · outbound

This paper cites In: CAiSE (2017).https://doi.org/10.1007/ 978-3-319-59536-8_30.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: CAiSE (2017).https://doi.org/10.1007/ 978-3-319-59536-8_30

Reference 27

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T15:39:21.146981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7593c88d-debb-4174-83e0-6c202060f177 · outbound

This paper cites ACM TKDD13(2) (2019).https: //doi.org/10.1145/3301300.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring ACM TKDD13(2) (2019).https: //doi.org/10.1145/3301300

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T15:39:20.483503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67380649-fa68-45d1-ad0a-6d983fde3078 · outbound

This paper cites Signal processing167(2020).https://doi.org/10.1016/j.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Signal processing167(2020).https://doi.org/10.1016/j

Reference 29

Resolution: malformed identifier; no resolver link, observed 2026-08-15T15:39:20.487410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1322c4e4-4fda-46e5-b434-2cce33c19e88 · outbound

This paper cites Nature Machine Intelligence5(3) (2023).https://doi.org/10.1038/s42256-023-00620-w.

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring Nature Machine Intelligence5(3) (2023).https://doi.org/10.1038/s42256-023-00620-w

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T15:39:20.491587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 695976c2-aa30-4a12-9865-dfe6cc1c2678 · outbound

This paper cites In: ICSOC (2021).

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring In: ICSOC (2021)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T15:39:21.132658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 04c2b439-d3fb-45e7-a45d-67f3f29430c0 · outbound

This paper cites lstm (with at- tention).

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring lstm (with at- tention)

Reference 32

Resolution: verified exact; doi, observed 2026-08-15T15:39:20.534092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.