@inproceedings{SchoenmannLodesSchiendorfer2023, author = {Sch{\"o}nmann, Alexander and Lodes, Lukas and Schiendorfer, Alexander}, title = {KI-Augmentation der Reifegradbewertung von Technologien: K{\"o}nnen KI-Sprachmodelle Expertenwissen ersetzen?}, booktitle = {Vorausschau und Technologieplanung: 17. Symposium f{\"u}r Vorausschau und Technologieplanung, 14. und 15. September 2023, Berlin}, editor = {Dumitrescu, Roman and H{\"o}lzle, Katharina}, publisher = {Universit{\"a}t Paderborn}, address = {Paderborn}, isbn = {978-3-947647-32-3}, doi = {https://doi.org/10.17619/UNIPB/1-1821}, pages = {371 -- 387}, year = {2023}, language = {de} } @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} } @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{LodesSchiendorfer2024, author = {Lodes, Lukas and Schiendorfer, Alexander}, title = {A multi-layer machine learning architecture for near real-time inference in manufacturing based on Apache Kafka and selective classification}, booktitle = {2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA)}, editor = {Facchinetti, Tullio and Cenedese, Angelo and Lo Bello, Lucia and Vitturi, Stefano and Sauter, Thilo and Tramarin, Federico}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-6123-0}, doi = {https://doi.org/10.1109/ETFA61755.2024.10711030}, year = {2024}, language = {en} }