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A. C. Amakor, K. Sonntag, and S. Peitz, “A multiobjective continuation method to compute the regularization path of deep neural networks,” arXiv. 2023.
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D. L. Ermel, Entwicklung und Evaluation eines Fachpraktikums für das Lehramtsstudium Technik zur Förderung fachpraktischer Kompetenzen. RWTH Aachen University, 2023.
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J. N. Schneider, Green-4-Future. Learning/Teaching/Training Activity –Transnational Training Event LTT. Business CANVAS Model – in Green Entrepreneurships. Paderborn, Germany, 2023.
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J. N. Schneider, Green-4-Future. Learning/Teaching/Training Activity –Transnational Training Event LTT. Creative Thinking – in Green Entrepreneurships. Paderborn, Germany, 2023.
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J. N. Schneider, Green-4-Future. Learning/Teaching/Training Activity –Transnational Training Event LTT. Use, sharing and creating Open Educational Resources – Why is it so important? . Paderborn, Germany, 2023.
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J. N. Schneider, SAFE -Streaming approaches for Europe - Enhancing the digital competences by streaming approaches for schools to tackle the challenges of COVID-19. The SAFE Learning Videos. Cologne, Germany, 2023.
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J. M. Hanselle, J. Fürnkranz, and E. Hüllermeier, “Probabilistic Scoring Lists for Interpretable Machine Learning,”10.1007/978-3-031-45275-8_13__in 26th International Conference on Discovery Science , Porto, 2023, vol. 14050, pp. 189–203, doi:10.1007/978-3-031-45275-8_13 .
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D. Johnson, O. Hakobyan, and H. Drimalla, “Towards Interpretability in Audio and Visual Affective Machine Learning: A Review.” 2023.
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J. Hanselle, J. Kornowicz, S. Heid, K. Thommes, and E. Hüllermeier, “Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain,” presented at the Lernen, Wissen, Daten, Analysen 2023, Marburg, Germany, 2023.
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M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, SAGE: An Incremental Version of SAGE for Online Explanation on Data Streams,” in Machine Learning and Knowledge Discovery in Databases: Research Track, Cham: Springer Nature Switzerland, 2023.
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F. Fumagalli, M. Muschalik, E. Hüllermeier, and B. Hammer, “Incremental permutation feature importance (iPFI): towards online explanations on data streams,”10.1007/s10994-023-06385-y__Machine Learning, vol. 112, no. 12, pp. 4863–4903, 2023, doi:10.1007/s10994-023-06385-y .
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F. Fumagalli, M. Muschalik, E. Hüllermeier, and B. Hammer, “On Feature Removal for Explainability in Dynamic Environments,”10.14428/esann/2023.es2023-148__presented at the ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium) and online, 2023, doi:10.14428/esann/2023.es2023-148 .
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C. Demir, M. Wiebesiek, R. Lu, A.-C. Ngonga Ngomo, and S. Heindorf, “LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals,” ECML PKDD, 2023.
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M. Sengupta, M. Alshomary, I. Scharlau, and H. Wachsmuth, “Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms,”10.18653/v1/2023.findings-emnlp.308__2023, doi:10.18653/v1/2023.findings-emnlp.308 .
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M. V. Seiler, J. Rook, J. Heins, O. L. Preuß, J. Bossek, and H. Trautmann, “Using Reinforcement Learning for Per-Instance Algorithm Configuration on the TSP,”10.1109/SSCI52147.2023.10372008__in 2023 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 361–368, doi:10.1109/SSCI52147.2023.10372008 .
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