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Email:
matthias.jakobs
tu-dortmund.de
Phone: 0231/755-8256 Room-No.: JvF25 R121 |
| Fischer/etal/2023a | Raphael Fischer and Jakobs, Matthias and Morik, Katharina. Energy efficiency considerations for popular AI benchmarks. In AAAI-2 Workshop AI for Innovation, 2023. |
| Fischer/etal/2022a | Fischer, Raphael and Jakobs, Matthias and Mücke, Sascha and Morik, Katharina. A Unified Framework for Assessing Energy Efficiency of Machine Learning. In Proceedings of the ECML Workshop on Data Science for Social Good, 2022. |
| Jakobs/etal/2022a | Jakobs, Matthias and Kotthaus, Helena and Röder, Ines and Baritz, Maximilian. SancScreen: Towards a real-world dataset for evaluating explainability methods. In Proceedings of the Conference on "Lernen, Wissen, Daten, Analysen" (LWDA), 2022. |
| Morik/etal/2021a |
Morik, Katharina and Kotthaus, Helena and Fischer, Raphael and Mücke, Sascha and Jakobs, Matthias and Piatkowski, Nico and Pauly, Andreas and Heppe, Lukas and Heinrich, Danny.
Yes We Care! - Certification for Machine Learning Methods through the Care Label Framework.
In
Elisa Fromont (editors),
Frontiers in Artificial Intelligence,
Frontiers,
2022.
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| Saadallah/etal/2022b |
Saadallah, Amal and Jakobs, Matthias and Morik, Katharina.
Explainable Online Ensemble of Deep Neural Network Pruning for Time Series Forecasting.
2022.
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| Wilking/etal/2022a | Wilking, Rahel and Jakobs, Matthias and Morik, Katharina. Fooling Perturbation-Based Explainability Methods. In Workshop on Trustworthy Artificial Intelligence as a part of the ECML/PKDD 22 program, 2022. |
| Beckh/etal/2021a |
Beckh, Katharina and Müller, Sebastian and Jakobs, Matthias and Toborek, Vanessa and Tan, Hanxiao and Fischer, Raphael and Welke, Pascal and Houben, Sebastian and von Rueden, Laura.
Explainable Machine Learning with Prior Knowledge: An Overview.
arXiv,
2021.
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| Saadallah/2021a | Saadallah, Amal and Jakobs, Matthias and Morik, Katharina. Explainable Online Deep Neural Network Selection using Adaptive Saliency Maps for Time Series Forecasting. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2021. |
| Fischer/etal/2020a |
Fischer, Raphael and Jakobs, Matthias and Mücke, Sascha and Morik, Katharina.
Solving Abstract Reasoning Tasks with Grammatical Evolution.
In
Trabold, Daniel and Welke, Pascal and Piatkowski, Nico (editors),
Proceedings of the LWDA 2020 Workshops: KDML, FGWM, FGWI-BIA, and FGDB,
pages 6--10,
2020.
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