Botache, D., Decke, J., Ripken, W., Dornipati, A., Götz-Hahn, F., Ayeb, M. & Sick, B. (2024). Enhancing Multi-Objective Optimization through Machine Learning-Supported Multiphysics Simulation. arXiv e-prints, , arXiv:2309.13179v2.

Decke, J., Jenß, A., Sick, B. & Gruhl, C. (2024). An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing. International Conference on Architecture of Computing Systems (ARCS), : Springer.

Decke, J., Wünsch, O., Sick, B. & Gruhl, C. (2024). From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs. International Conference on Architecture of Computing Systems (ARCS), : Springer.

Heidecker, F., El-Khateeb, A., Bieshaar, M. & Sick, B. (2024). Criteria for Uncertainty-based Corner Cases Detection in Instance Segmentation. arXiv e-prints, , arXiv:2404.11266.

Magnussen, B. M., Möckel, F., Jessulat, M., Stern, C. & Sick, B. (2024). Optical Detection of the Body Mass Index and Related Parameters Using Multiple Spatially Resolved Reflection Spectroscopy. International Conference on Bioinformatics and Computational Biology (ICBCB), : IEEE.

Aßenmacher, M., Rauch, L., Goschenhofer, J., Stephan, A., Bischl, B., Roth, B. & Sick, B. (2023). Towards Enhancing Deep Active Learning with Weak Supervision and Constrained Clustering. Workshop on Interactive Adapative Learning (IAL), ECML PKDD (p./pp. 65--73), .

Breitenstein, J., Heidecker, F., Lyssenko, M., Bogdoll, D., Bieshaar, M., Zöllner, J. M., Sick, B. & Fingscheidt, T. (2023). What Does Really Count? Estimating Relevance of Corner Cases for Semantic Segmentation in Automated Driving. Workshop on roBustness and Reliability of Autonomous Vehicles in the Open-world (BRAVO), ICCV (p./pp. 3991--4000), .

Decke, J., Gruhl, C., Rauch, L. & Sick, B. (2023). DADO – Low-Cost Query Strategies for Deep Active Design Optimization. International Conference on Machine Learning and Applications (ICMLA) (p./pp. 1611--1618), : IEEE.

Deinzer, R., Eidenhardt, Z., Sohrabi, K., Stenger, M., Kraft, D., Sick, B., Götz-Hahn, F., Bottenbruch, C., Berneburg, N. & Weik, U. (2023). It is the habit not the handle that affects tooth brushing. Results of a randomised counterbalanced cross over study. Research Square, . doi: 10.21203/rs.3.rs-3491691/v1

Heidecker, F., Susetzky, T., Fuchs, E. & Sick, B. (2023). Context Information for Corner Case Detection in Highly Automated Driving. IEEE International Conference on Intelligent Transportation Systems (ITSC) (p./pp. 1522--1529), : IEEE.

Heidecker, F., Bieshaar, M. & Sick, B. (2023). Corner Cases in Machine Learning Processes. AI Perspectives & Advances, 6, 1--17. doi: 10.1186/s42467-023-00015-y

Huang, Z., He, Y. & Sick, B. (2023). Spatio-Temporal Attention Graph Neural Network for Remaining Useful Life Prediction. Computational Science and Computational Intelligence (CSCI), : IEEE.

Magnussen, B. M., Stern, C. & Sick, B. (2023). Continuous Feature Networks: A Novel Method to Process Irregularly and Inconsistently Sampled Data With Position-Dependent Features. International Journal On Advances in Intelligent Systems, 16, 43--50.

Magnussen, B. M., Stern, C. & Sick, B. (2023). Leveraging Repeated Unlabelled Noisy Measurements to Augment Supervised Learning. International Conference on Computational Intelligence and Intelligent Systems (CIIS) (p./pp. 1--6), : ACM.

Nivarthi, C. P., Vogt, S. & Sick, B. (2023). Multi-Task Representation Learning for Renewable-Power Forecasting: A Comparative Analysis of Unified Autoencoder Variants and Task-Embedding Dimensions. Machine Learning and Knowledge Extraction (MAKE), 5, 1214--1233. doi: 10.3390/make5030062

Nivarthi, C. P. & Sick, B. (2023). Towards Few-Shot Time Series Anomaly Detection with Temporal Attention and Dynamic Thresholding. International Conference on Machine Learning and Applications (ICMLA) (p./pp. 1444--1450), : IEEE.

Schreck, S., Reichert, H., Hetzel, M., Doll, K. & Sick, B. (2023). Height Change Feature Based Free Space Detection. International Conference on Control, Mechatronics and Automation (ICCMA) (p./pp. 171--176), : IEEE.

Schreiber, J. & Sick, B. (2023). Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts. Energy and AI, 14, 100249. doi: 10.1016/j.egyai.2023.100249

Loeser, I., Braun, M., Gruhl, C., Menke, J.-H., Sick, B. & Tomforde, S. (2022). The Vision of Self-Management in Cognitive Organic Power Distribution Systems. Energies, 15, 881. doi: 10.3390/en15030881

Pham, T., Kottke, D., Krempl, G. & Sick, B. (2022). Stream-based active learning for sliding windows under the influence of verification latency. Machine Learning, 111, 2011--2036. doi: doi.org/10.1007/s10994-021-06099-z