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ORCIDJari Peeperkorn
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2020 – today
- 2024
[j3]Jari Peeperkorn
, Seppe vanden Broucke, Jochen De Weerdt:
Validation set sampling strategies for predictive process monitoring. Inf. Syst. 121: 102330 (2024)- [i5]Lien Bosmans, Jari Peeperkorn, Alexandre Goossens, Giovanni Lugaresi, Johannes De Smedt, Jochen De Weerdt:
Dynamic and Scalable Data Preparation for Object-Centric Process Mining. CoRR abs/2410.00596 (2024) - 2023
- [j2]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Global conformance checking measures using shallow representation and deep learning. Eng. Appl. Artif. Intell. 123(Part): 106393 (2023) - [j1]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Can recurrent neural networks learn process model structure? J. Intell. Inf. Syst. 61(1): 27-51 (2023) - [c10]Jan Niklas Adams, Jari Peeperkorn, Tobias Brockhoff, Isabelle Terrier, Heiko Göhner, Merih Seran Uysal, Seppe vanden Broucke, Jochen De Weerdt, Wil M. P. van der Aalst:
Discovering high-quality process models despite data scarcity. ER (Companion) 2023 - [c9]Thais Rodrigues Neubauer, Jari Peeperkorn, Sarajane Marques Peres, Jochen De Weerdt, Marcelo Fantinato:
Vector Representation for Business Process: Graph Embedding for Domain Knowledge Integration. ICMLA 2023: 588-594 - [c8]Alexander Stevens, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt:
Manifold Learning for Adversarial Robustness in Predictive Process Monitoring. ICPM 2023: 17-24 - [i4]Jan Niklas Adams, Jari Peeperkorn, Tobias Brockhoff, Isabelle Terrier, Heiko Göhner, Merih Seran Uysal, Seppe vanden Broucke, Jochen De Weerdt, Wil M. P. van der Aalst:
Discovering High-Quality Process Models Despite Data Scarcity. CoRR abs/2310.11332 (2023) - 2022
- [c7]Jarne Vandenabeele, Gilles Vermaut, Jari Peeperkorn, Jochen De Weerdt:
Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors. ATAED@Petri Nets 2022: 9-24 - [c6]Alexander Stevens, Johannes De Smedt, Jari Peeperkorn, Jochen De Weerdt:
Assessing the Robustness in Predictive Process Monitoring through Adversarial Attacks. ICPM 2022: 56-63 - [c5]Jari Peeperkorn, Carlos Ortega Vázquez, Alexander Stevens, Johannes De Smedt, Seppe vanden Broucke, Jochen De Weerdt:
Outcome-Oriented Predictive Process Monitoring on Positive and Unlabelled Event Logs. ICPM Workshops 2022: 255-268 - [i3]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Can deep neural networks learn process model structure? An assessment framework and analysis. CoRR abs/2202.11985 (2022) - [i2]Jarne Vandenabeele, Gilles Vermaut, Jari Peeperkorn, Jochen De Weerdt:
Enhancing Stochastic Petri Net-based Remaining Time Prediction using k-Nearest Neighbors. CoRR abs/2206.13109 (2022) - [i1]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Can recurrent neural networks learn process model structure? CoRR abs/2212.06430 (2022) - 2021
- [c4]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Can Deep Neural Networks Learn Process Model Structure? An Assessment Framework and Analysis. ICPM Workshops 2021: 127-139 - [c3]Alexander Stevens, Johannes De Smedt, Jari Peeperkorn:
Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring. ICPM Workshops 2021: 194-206 - 2020
- [c2]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Conformance Checking Using Activity and Trace Embeddings. BPM (Forum) 2020: 105-121 - [c1]Jari Peeperkorn, Seppe vanden Broucke, Jochen De Weerdt:
Supervised Conformance Checking Using Recurrent Neural Network Classifiers. ICPM Workshops 2020: 175-187
Coauthor Index
aka: Seppe vanden Broucke
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