| Predicting Flow Stress Behavior of an AA7075 Alloy Using Machine Learning Methods J Decke, A Engelhardt, L Rauch, S Degener, SV Sajadifar, E Scharifi, ... Crystals 12 (9), 1281, 2022 | 6 | 2022 |
| Enhancing Active Learning with Weak Supervision and Transfer Learning by Leveraging Information and Knowledge Sources. L Rauch, D Huseljic, B Sick IAL@ PKDD/ECML, 27-42, 2022 | 3 | 2022 |
| ActiveGLAE: A Benchmark for Deep Active Learning with Transformers L Rauch, M Aßenmacher, D Huseljic, M Wirth, B Bischl, B Sick Joint European Conference on Machine Learning and Knowledge Discovery in …, 2023 | 2 | 2023 |
| Active Bird2Vec: Towards end-to-end bird sound monitoring with transformers L Rauch, R Schwinger, M Wirth, B Sick, S Tomforde, C Scholz arXiv preprint arXiv:2308.07121, 2023 | 2 | 2023 |
| Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image Classification D Huseljic, P Hahn, M Herde, L Rauch, B Sick arXiv preprint arXiv:2404.08981, 2024 | | 2024 |
| BirdSet: A Multi-Task Benchmark for Classification in Avian Bioacoustics L Rauch, R Schwinger, M Wirth, R Heinrich, J Lange, S Kahl, B Sick, ... arXiv preprint arXiv:2403.10380, 2024 | | 2024 |
| DADO–Low-Cost Query Strategies for Deep Active Design Optimization J Decke, C Gruhl, L Rauch, B Sick 2023 International Conference on Machine Learning and Applications (ICMLA …, 2023 | | 2023 |
| DADO--Low-Cost Selection Strategies for Deep Active Design Optimization J Decke, C Gruhl, L Rauch, B Sick arXiv preprint arXiv:2307.04536, 2023 | | 2023 |
| Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering M Aßenmacher, L Rauch, J Goschenhofer, A Stephan, B Bischl, B Roth, ... | | 2023 |
