Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M., Tornede, A., &; Wever, M. D. (2023). Configuration and Evaluation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, &; H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn.https://doi.org/10.5281/zenodo.8068466 

Haake, C.-J., Hehenkamp, B., &; Polevoy, G. (2023). The Market for Services: Incentives, Algorithms, Implementation. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, &; H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 21–44). Heinz Nixdorf Institut, Universität Paderborn.https://doi.org/10.5281/zenodo.8068414 

Wehrheim, H., Hüllermeier, E., Becker, S., Becker, M., Richter, C., &; Sharma, A. (2023). Composition Analysis in Unknown Contexts. In C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, &; H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp. 105–123). Heinz Nixdorf Institut, Universität Paderborn.https://doi.org/10.5281/zenodo.8068510 

Raeisi Nafchi, M. (2023). Reconfigurable Random Forest Implementation on FPGA. Paderborn University.

Yadalam Murali Kumar, N. (2023). Data Analytics for Predictive Maintenance of Time Series Data. Paderborn University.

Bittel, L., Gharibian, S., &; Kliesch, M. (2023). The Optimal Depth of Variational Quantum Algorithms Is QCMA-Hard to Approximate. Proceedings of the 38th Computational Complexity Conference (CCC), 264(34), 34:1-34:24.https://doi.org/10.4230/LIPIcs.CCC.2023.34 

Pukrop, S. (2023). On Cloud Assisted, Restricted, and Reosurce Constrained Scheduling.https://doi.org/10.17619/UNIPB/1-1768  

Schneider, F. (2023). Utilizing Redundancy in Distributed Heterogeneous Storage.

Ranade, A. (2023). Graph Neural Network-based Anomaly Detection in  Smart Grid Energy Consumption.

Ali, O. (2023). Highly accurate deep compressed facial recognition.

Ashri, N. (2023). Virtual On-Demand Volunteer System Based on Delaunay Triangulation.

Meyer, M., Kenter, T., &; Plessl, C. (2023). Multi-FPGA Designs and Scaling of HPC Challenge Benchmarks via MPI and Circuit-Switched Inter-FPGA Networks. ACM Transactions on Reconfigurable Technology and Systems.https://doi.org/10.1145/3576200 

N., N. (2023). Improving the End-of-Line Test of Custom-Built Geared Motors using Clustering based on Neural Networks.

Demir, C., &; Ngonga Ngomo, A.-C. (2023). Neuro-Symbolic Class Expression Learning. International Joint Conference on Artificial Intelligence. International Joint Conference on Artificial Intelligence IJCAI 2023, Macau.

Wu, X., Kenter, T., Schade, R., Kühne, T., &; Plessl, C. (2023). Computing and Compressing Electron Repulsion Integrals on FPGAs. 2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 162–173.https://doi.org/10.1109/FCCM57271.2023.00026 

Schade, R., Kenter, T., Elgabarty, H., Lass, M., Kühne, T., &; Plessl, C. (2023). Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics. The International Journal of High Performance Computing Applications, Article 109434202311776.https://doi.org/10.1177/10943420231177631 

Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.https://doi.org/10.17619/UNIPB/1-1780  

Peitz, S., Harder, H., Nüske, F., Philipp, F., Schaller, M., &; Worthmann, K. (2023). Partial observations, coarse graining and equivariance in Koopman  operator theory for large-scale dynamical systems. In arXiv:2307.15325.

Nüske, F., Peitz, S., Philipp, F., Schaller, M., &; Worthmann, K. (2023). Finite-data error bounds for Koopman-based prediction and control. Journal of Nonlinear Science, 33, Article 14.https://doi.org/10.1007/s00332-022-09862-1 

Hotegni, S. S., Peitz, S., &; Berkemeier, M. B. (2023). Multi-Objective Optimization for Sparse Deep Neural Network Training. In arXiv:2308.12243.