TY - CHAP A1 - Schiendorfer, Alexander A1 - Lassner, Christoph A1 - Anders, Gerrit A1 - Reif, Wolfgang A1 - Lienhart, Rainer ED - Cardoso, João M. P. T1 - Active Learning for Abstract Models of Collectives T2 - ARCS 2015 - 28th International Conference on Architecture of Computing Systems, Workshop Proceedings Y1 - 2015 UR - https://www.vde-verlag.de/proceedings-de/563657010.html SN - 978-3-8007-3657-7 N1 - Auch veröffentlicht auf IEEE: https://ieeexplore.ieee.org/abstract/document/7107102 PB - VDE Verlag CY - Berlin ER - TY - CHAP A1 - Gajek, Carola A1 - Schiendorfer, Alexander A1 - Reif, Wolfgang ED - Amini, Massih-Reza ED - Canu, Stéphane ED - Fischer, Asja ED - Guns, Tias ED - Kralj Novak, Petra ED - Tsoumakas, Grigorios T1 - A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks T2 - Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Proceedings, Part I UR - https://doi.org/10.1007/978-3-031-26387-3_28 KW - Graph Machine Learning KW - Recommendation KW - Computer-aided Design KW - AI-aided Design Y1 - 2023 UR - https://doi.org/10.1007/978-3-031-26387-3_28 SN - 978-3-031-26387-3 SN - 978-3-031-26386-6 SP - 457 EP - 473 PB - Springer CY - Cham ER - TY - CHAP A1 - Lodes, Lukas A1 - Schiendorfer, Alexander ED - Do, Phuc ED - Michau, Gabriel ED - Ezhilarasu, Cordelia T1 - Certainty Groups: A Practical Approach to Distinguish Confidence Levels in Neural Networks T2 - Proceedings of the European Conference of the PHM Society 2022 N2 - Machine Learning (ML), in particular classification with deep neural nets, can be applied to a variety of industrial tasks. It can augment established methods for controlling manufacturing processes such as statistical process control (SPC) to detect non-obvious patterns in high-dimensional input data. However, due to the widespread issue of model miscalibration in neural networks, there is a need for estimating the predictive uncertainty of these models. Many established approaches for uncertainty estimation output scores that are difficult to put into actionable insight. We therefore introduce the concept of certainty groups which distinguish the predictions of a neural network into the normal group and the certainty group. The certainty group contains only predictions with a very high accuracy that can be set up to 100%. We present an approach to compute these certainty groups and demonstrate our approach on two datasets from a PHM setting. UR - https://doi.org/10.36001/phme.2022.v7i1.3331 KW - machine Learning KW - classification KW - uncertainty KW - estimaton KW - neural network Y1 - 2022 UR - https://doi.org/10.36001/phme.2022.v7i1.3331 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31307 SN - 978-1-936263-36-3 SP - 294 EP - 305 PB - PHM Society CY - State College ER - TY - CHAP A1 - Bhavnani, Sidhant A1 - Schiendorfer, Alexander ED - Schaus, Pierre T1 - Towards copeland optimization in combinatorial problems T2 - Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022; Proceedings UR - https://doi.org/10.1007/978-3-031-08011-1_4 KW - constraint programming KW - social choice theory KW - OR Y1 - 2022 UR - https://doi.org/10.1007/978-3-031-08011-1_4 SN - 978-3-031-08010-4 SN - 978-3-031-08011-1 N1 - Access provided by Nationallizenz Ebooks Medicine SP - 34 EP - 43 PB - Springer CY - Cham ER - TY - CHAP A1 - Dachtler, Kristina A1 - Ortner, Michael A1 - Ferri, Massimo A1 - Eberst, Christof A1 - Schiendorfer, Alexander T1 - Data-centric and Goal-oriented AI for Robotic Repair Tasks T2 - ISR Europe 2023, 56th International Symposium on Robotics, in cooperation with Fraunhofer IPA September 26 – 27, 2023 in Stuttgart Y1 - 2023 SN - 978-3-8007-6141-8 N1 - Auch veröffentlicht auf IEEE: https://ieeexplore.ieee.org/document/10363053 PB - VDE Verlag CY - Berlin ER - TY - CHAP A1 - Lodes, Lukas A1 - Schiendorfer, Alexander ED - Kinnaird, Katherine M. ED - Steinbach, Peter ED - Guhr, Oliver T1 - A Deep Learning Bootcamp for Engineering & Management Students T2 - Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop Y1 - 2023 UR - https://proceedings.mlr.press/v207/lodes23a.html SP - 32 EP - 36 PB - PMLR CY - [s. l.] ER -