@inproceedings{SchiendorferLassnerAndersetal.2015, author = {Schiendorfer, Alexander and Lassner, Christoph and Anders, Gerrit and Reif, Wolfgang and Lienhart, Rainer}, title = {Active Learning for Abstract Models of Collectives}, booktitle = {ARCS 2015 - 28th International Conference on Architecture of Computing Systems, Workshop Proceedings}, editor = {Cardoso, Jo{\~a}o M. P.}, publisher = {VDE Verlag}, address = {Berlin}, isbn = {978-3-8007-3657-7}, url = {https://www.vde-verlag.de/proceedings-de/563657010.html}, year = {2015}, language = {en} } @inproceedings{GajekSchiendorferReif2023, author = {Gajek, Carola and Schiendorfer, Alexander and Reif, Wolfgang}, title = {A Recommendation System for CAD Assembly Modeling based on Graph Neural Networks}, booktitle = {Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2022, Proceedings, Part I}, editor = {Amini, Massih-Reza and Canu, St{\´e}phane and Fischer, Asja and Guns, Tias and Kralj Novak, Petra and Tsoumakas, Grigorios}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-26387-3}, doi = {https://doi.org/10.1007/978-3-031-26387-3_28}, pages = {457 -- 473}, year = {2023}, language = {en} } @inproceedings{LodesSchiendorfer2022, author = {Lodes, Lukas and Schiendorfer, Alexander}, title = {Certainty Groups: A Practical Approach to Distinguish Confidence Levels in Neural Networks}, booktitle = {Proceedings of the European Conference of the PHM Society 2022}, editor = {Do, Phuc and Michau, Gabriel and Ezhilarasu, Cordelia}, publisher = {PHM Society}, address = {State College}, isbn = {978-1-936263-36-3}, doi = {https://doi.org/10.36001/phme.2022.v7i1.3331}, pages = {294 -- 305}, year = {2022}, abstract = {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.}, language = {en} } @inproceedings{BhavnaniSchiendorfer2022, author = {Bhavnani, Sidhant and Schiendorfer, Alexander}, title = {Towards copeland optimization in combinatorial problems}, booktitle = {Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 19th International Conference, CPAIOR 2022, Los Angeles, CA, USA, June 20-23, 2022; Proceedings}, editor = {Schaus, Pierre}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-08010-4}, doi = {https://doi.org/10.1007/978-3-031-08011-1_4}, pages = {34 -- 43}, year = {2022}, language = {en} } @inproceedings{DachtlerOrtnerFerrietal.2023, author = {Dachtler, Kristina and Ortner, Michael and Ferri, Massimo and Eberst, Christof and Schiendorfer, Alexander}, title = {Data-centric and Goal-oriented AI for Robotic Repair Tasks}, booktitle = {ISR Europe 2023, 56th International Symposium on Robotics, in cooperation with Fraunhofer IPA September 26 - 27, 2023 in Stuttgart}, publisher = {VDE Verlag}, address = {Berlin}, isbn = {978-3-8007-6141-8}, year = {2023}, language = {en} } @inproceedings{LodesSchiendorfer2023, author = {Lodes, Lukas and Schiendorfer, Alexander}, title = {A Deep Learning Bootcamp for Engineering \& Management Students}, booktitle = {Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop}, editor = {Kinnaird, Katherine M. and Steinbach, Peter and Guhr, Oliver}, publisher = {PMLR}, address = {[s. l.]}, url = {https://proceedings.mlr.press/v207/lodes23a.html}, pages = {32 -- 36}, year = {2023}, language = {en} }