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