Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders | Paper Citation Record

Observation 0513232e-ee45-4312-bbf5-16a75539c472 · outbound

This paper cites In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.739036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7ea3a52d-26b5-4bcc-94ae-f27d1eeeb583 · outbound

This paper cites In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.721805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b17a1abb-4be4-405d-9a43-ca9c28795680 · outbound

This paper cites In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alch ´e-Buc, F., Fox, E.B., Gar- nett, R.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Wallach, H.M., Larochelle, H., Beygelzimer, A., d’Alch ´e-Buc, F., Fox, E.B., Gar- nett, R

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.698392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation fc24dbf0-4297-4335-b848-24a69f0de86f · outbound

This paper cites In: International Conference on Machine Learning.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: International Conference on Machine Learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.675041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1ec8519e-dd19-4f7b-b0d8-f2d2b852b9c9 · outbound

This paper cites In: International conference on artificial intelligence and statistics.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: International conference on artificial intelligence and statistics

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.650711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 535c074b-5d52-4104-933c-dae3dd029a5f · outbound

This paper cites Information Systems 56, 235–257 (2016).

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Information Systems 56, 235–257 (2016)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.615700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7b501713-23c7-4f93-b5c2-e5b7c64f1c29 · outbound

This paper cites In: Bengio, S., Wal- lach, H.M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Gar- nett, R.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Bengio, S., Wal- lach, H.M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Gar- nett, R

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.594049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e99f4b27-f599-4224-ab91-ad9417e3f2dc · outbound

This paper cites an unresolved cited work.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:25:06.571997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6ff6b250-1080-4acb-a3ee-ee6ad72329cd · outbound

This paper cites In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Workshop Track Proceedings.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Workshop Track Proceedings

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.540312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f7fa83de-7677-4978-a978-87e987ad70b5 · outbound

This paper cites In: Bengio, Y ., LeCun, Y.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Bengio, Y ., LeCun, Y

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.516828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7e2dda0f-4297-4f34-9d99-1cd2217a0bc9 · outbound

This paper cites an unresolved cited work.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:04.984257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48481789-15b6-4ecb-a2c6-b1640c3a9a7d · outbound

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

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:04.990265Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 802fba01-fcd5-4756-bc9d-89e6ab6adac0 · outbound

This paper cites IEEE transactions on neural networks and learning systems 27(6), 1333–1344 (2015).

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders IEEE transactions on neural networks and learning systems 27(6), 1333–1344 (2015)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.484702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ceb4549b-c87a-4eea-8c5c-18aa6a7f802f · outbound

This paper cites Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:05.000655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8c99c85-4aef-4c91-b144-cae09e88a8bd · outbound

This paper cites In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Indulska, M., Reinhartz-Berger, I., Cetina, C., Pastor, O

Reference 15

Resolution: malformed identifier; doi_truncated, observed 2026-08-12T15:25:05.344694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation dfb0ab03-523b-4f4a-aa70-7d99f1af7e13 · outbound

This paper cites Auto-Encoding Variational Bayes.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Auto-Encoding Variational Bayes

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:05.012595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80e3c66f-2234-4b4f-8b1b-8bf1ce13a003 · outbound

This paper cites an unresolved cited work.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Unresolved cited work

Reference 17

Resolution: malformed identifier; doi_truncated, observed 2026-08-12T15:25:05.325187Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 60e85a94-5042-45bf-8deb-dda10cf74a42 · outbound

This paper cites Business & Information Systems Engineering 63, 261–276 (2021).

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Business & Information Systems Engineering 63, 261–276 (2021)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.460566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3ecd133c-e893-4818-bc3c-caaef6e43ce1 · outbound

This paper cites Interpretable Artificial Intelligence: A Perspective of Granular Computing pp.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Interpretable Artificial Intelligence: A Perspective of Granular Computing pp

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.439612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 58a373f3-0717-462a-9b60-ec96740be2a8 · outbound

This paper cites Artificial Intelligence Review 55(2), 801–827 (2022).

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Artificial Intelligence Review 55(2), 801–827 (2022)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.406843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8c72e16d-fec3-4241-93f8-e14400ad5acc · outbound

This paper cites In: Van- schoren, J., Yeung, S.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Van- schoren, J., Yeung, S

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.383759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f78bba33-e830-401e-b5dc-5016011e5ad5 · outbound

This paper cites In: Huang, Y ., King, I., Liu, T., van Steen, M.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Huang, Y ., King, I., Liu, T., van Steen, M

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:05.044690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5477a78-8c3e-47d2-8c32-0432e35790a7 · outbound

This paper cites an unresolved cited work.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Unresolved cited work

Reference 23

Resolution: verified exact; doi, observed 2026-08-12T15:25:05.306139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c1d303d7-2df4-416d-8d4f-3aa4b4f07e48 · outbound

This paper cites How do I update my model? On the resilience of Predictive Process Monitoring models to change.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders How do I update my model? On the resilience of Predictive Process Monitoring models to change

Reference 24

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T15:25:05.782190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 50438cfc-deaf-403b-811e-10f32268fbd8 · outbound

This paper cites an unresolved cited work.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Unresolved cited work

Reference 25

Resolution: malformed identifier; no resolver link, observed 2026-08-12T15:25:05.065548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70d4df2f-026b-477b-a7f0-d0dc97f697fe · outbound

This paper cites Harvard Data Science Review 2(1), 1 (2020).

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Harvard Data Science Review 2(1), 1 (2020)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.362453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0362d34d-e6c2-4bfd-8b74-135bf48b077a · outbound

This paper cites In: Seventh International Conference on Learning Representations (ICLR 2019).

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Seventh International Conference on Learning Representations (ICLR 2019)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.344026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 8d0a89c4-1a0f-437c-aafd-363c29b5a6ec · outbound

This paper cites In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.325103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 606144e8-dd0a-40b7-ad4d-4409122d9a8c · outbound

This paper cites European Journal of Operational Research 317(2), 317–329 (2024).

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders European Journal of Operational Research 317(2), 317–329 (2024)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.283842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 49dda6aa-5333-4888-9445-199518c15ab0 · outbound

This paper cites In: 2022 4th International Conference on Process Mining (ICPM).

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: 2022 4th International Conference on Process Mining (ICPM)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.261287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 17371246-3d04-4de5-904f-3cff4e589f1e · outbound

This paper cites Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Generating Feasible and Plausible Counterfactual Explanations for Outcome Prediction of Business Processes

Reference 31

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T15:25:05.753420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ece762b0-0ac0-49c1-9aff-b94cd3d88d91 · outbound

This paper cites In: 2023 5th International Conference on Process Mining (ICPM).

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: 2023 5th International Conference on Process Mining (ICPM)

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.236458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c9ac92f5-3a54-4c51-8552-c20be123223a · outbound

This paper cites In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20,.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.216686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d7d29181-206f-4714-b17b-5bc85d3ba752 · outbound

This paper cites In: Bengio, Y ., LeCun, Y.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Bengio, Y ., LeCun, Y

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.197467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7d62e633-5e28-45db-a700-8b0215cec146 · outbound

This paper cites In: Advanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Es- sen, Germany, June 12-16, 2017, Proceedings 29.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Advanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Es- sen, Germany, June 12-16, 2017, Proceedings 29

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.166336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6635008e-df53-4301-bad1-0156344098a8 · outbound

This paper cites In: Demeniconi, C., Davidson, I.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Demeniconi, C., Davidson, I

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:05.135098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2979702-2b9c-47db-91d8-34970b06761e · outbound

This paper cites In: Fahland, D., Ghidini, C., Becker, J., Dumas, M.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Fahland, D., Ghidini, C., Becker, J., Dumas, M

Reference 37

Resolution: malformed identifier; doi_truncated, observed 2026-08-12T15:25:05.266568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cfb48d75-282a-4d74-b007-517d18524c2f · outbound

This paper cites ACM Trans.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders ACM Trans

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:05.144568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf5d3274-8b59-4052-b728-cc333480b438 · outbound

This paper cites an unresolved cited work.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:25:06.140319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 56102d74-7524-4d84-9a10-9d6f2f42cd10 · outbound

This paper cites In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.112809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 84db5fc1-d720-4aa9-9b21-b105e6259a46 · outbound

This paper cites Conditional Generative Models for Counterfactual Explanations.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Conditional Generative Models for Counterfactual Explanations

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:05.160436Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d726b13d-5f44-477f-b609-4d4fbb477f4b · outbound

This paper cites In: Polyvyanyy, A., Wynn, M.T., Looy, A.V ., Reichert, M.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Polyvyanyy, A., Wynn, M.T., Looy, A.V ., Reichert, M

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:05.169260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6a671b9e-685a-40aa-b338-0573a8c29263 · outbound

This paper cites In: Bertino, E., Chang, C.K., Chen, P., Damiani, E., Goul, M., Oyama, K.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: Bertino, E., Chang, C.K., Chen, P., Damiani, E., Goul, M., Oyama, K

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:05.174580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a270d801-10ce-439f-9f8e-844f8d9a3ec0 · outbound

This paper cites lstm (with attention).

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders lstm (with attention)

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.089771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 21607e98-0a2a-4ac3-b600-b65d50e7d253 · outbound

This paper cites Information Sciences 587, 794–812 (2022).

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Information Sciences 587, 794–812 (2022)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.069929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9e1c6752-9a54-461c-b2be-8ed610e8a577 · outbound

This paper cites Generating Adversarial Examples with Adversarial Networks.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Generating Adversarial Examples with Adversarial Networks

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:05.190403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e68961e-5071-4d56-ab4b-f88355126a75 · outbound

This paper cites IEEE Trans.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders IEEE Trans

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:05.195753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 013a33d1-2155-4cda-9571-649132ff8ad0 · outbound

This paper cites In: International Conference on Learning Representations (2018).

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders In: International Conference on Learning Representations (2018)

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T15:25:06.046275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 64e49607-cd17-49c7-ad1a-df254c9e9bfe · outbound

This paper cites 6976–6987.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders 6976–6987

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-12T15:25:05.118008Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59f60466-7829-4e6d-aafd-e7e0d4a702c0 · outbound

This paper cites an unresolved cited work.

Generating Realistic Adversarial Examples for Business Processes using Variational Autoencoders Unresolved cited work

Reference 9119

Resolution: unresolved; raw_fallback, observed 2026-08-12T15:25:06.302482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers