Tim Pychynski

According to our database, Tim Pychynski authored at least 8 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

causalAssembly: Generating Realistic Production Data for Benchmarking Causal Discovery.

Konstantin Göbler, Tobias Windisch, Tim Pychynski, Steffen Sonntag, Martin Roth, Mathias Drton

CoRR, 2023

2022

Machine learning with domain knowledge for predictive quality monitoring in resistance spot welding.

Baifan Zhou, Tim Pychynski, Markus Reischl, Evgeny Kharlamov, Ralf Mikut

J. Intell. Manuf., 2022

2020

Semantic ML for Manufacturing Monitoring at Bosch.

Baifan Zhou, Yulia Svetashova, Tim Pychynski, Evgeny Kharlamov

Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice co-located with 19th International Semantic Web Conference (ISWC 2020), 2020

SemML: Reusable ML for Condition Monitoring in Discrete Manufacturing.

Yulia Svetashova, Baifan Zhou, Stefan Schmid, Tim Pychynski, Evgeny Kharlamov

Proceedings of the ISWC 2020 Demos and Industry Tracks: From Novel Ideas to Industrial Practice co-located with 19th International Semantic Web Conference (ISWC 2020), 2020

Ontology-Enhanced Machine Learning: A Bosch Use Case of Welding Quality Monitoring.

Yulia Svetashova, Baifan Zhou, Tim Pychynski, Stefan Schmidt, York Sure-Vetter, Ralf Mikut, Evgeny Kharlamov

Proceedings of the Semantic Web - ISWC 2020, 2020

SemFE: Facilitating ML Pipeline Development with Semantics.

Baifan Zhou, Yulia Svetashova, Tim Pychynski, Ildar Baimuratov, Ahmet Soylu, Evgeny Kharlamov

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

Predicting Quality of Automated Welding with Machine Learning and Semantics: A Bosch Case Study.

Baifan Zhou, Yulia Svetashova, Seongsu Byeon, Tim Pychynski, Ralf Mikut, Evgeny Kharlamov

Proceedings of the CIKM '20: The 29th ACM International Conference on Information and Knowledge Management, 2020

2017

DaMoQ: Eine Open-Source-MATLAB-Toolbox zur Bewertung von Daten- und Modellqualität in Regressionen.

Wolfgang Doneit, Ralf Mikut, Lutz Gröll, Tim Pychynski, Markus Reischl

Autom., 2017