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M. Raeisi Nafchi, Reconfigurable Random Forest Implementation on FPGA. Paderborn University, 2023.
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N. Yadalam Murali Kumar, Data Analytics for Predictive Maintenance of Time Series Data. Paderborn University, 2023.
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L. Bittel, S. Gharibian, and M. Kliesch, “The Optimal Depth of Variational Quantum Algorithms Is QCMA-Hard to Approximate,”10.4230/LIPIcs.CCC.2023.34__in Proceedings of the 38th Computational Complexity Conference (CCC), 2023, vol. 264, no. 34, p. 34:1-34:24, doi:10.4230/LIPIcs.CCC.2023.34 .
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S. Pukrop, On Cloud Assisted, Restricted, and Reosurce Constrained Scheduling. 2023.
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F. Schneider, Utilizing Redundancy in Distributed Heterogeneous Storage. 2023.
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A. Ranade, Graph Neural Network-based Anomaly Detection in  Smart Grid Energy Consumption. 2023.
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O. Ali, Highly accurate deep compressed facial recognition. 2023.
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N. Ashri, Virtual On-Demand Volunteer System Based on Delaunay Triangulation. 2023.
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M. Meyer, T. Kenter, and C. Plessl, “Multi-FPGA Designs and Scaling of HPC Challenge Benchmarks via MPI and Circuit-Switched Inter-FPGA Networks,”10.1145/3576200__ACM Transactions on Reconfigurable Technology and Systems, 2023, doi:10.1145/3576200 .
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N. N., Improving the End-of-Line Test of Custom-Built Geared Motors using Clustering based on Neural Networks. 2023.
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C. Demir and A.-C. Ngonga Ngomo, “Neuro-Symbolic Class Expression Learning,” International Joint Conference on Artificial Intelligence, 2023.
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X. Wu, T. Kenter, R. Schade, T. Kühne, and C. Plessl, “Computing and Compressing Electron Repulsion Integrals on FPGAs,”10.1109/FCCM57271.2023.00026__in 2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2023, pp. 162–173, doi:10.1109/FCCM57271.2023.00026 .
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R. Schade, T. Kenter, H. Elgabarty, M. Lass, T. Kühne, and C. Plessl, “Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics,”10.1177/10943420231177631__The International Journal of High Performance Computing Applications, Art. no. 109434202311776, 2023, doi:10.1177/10943420231177631 .
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A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions. 2023.
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R. H. Gusmita, A. F. Firmansyah, D. Moussallem, and A.-C. Ngonga Ngomo, “IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran,” in Natural Language Processing and Information Systems, Cham: Springer Nature Switzerland, 2023.
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S. Peitz, H. Harder, F. Nüske, F. Philipp, M. Schaller, and K. Worthmann, “Partial observations, coarse graining and equivariance in Koopman  operator theory for large-scale dynamical systems,” arXiv:2307.15325. 2023.
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F. Nüske, S. Peitz, F. Philipp, M. Schaller, and K. Worthmann, “Finite-data error bounds for Koopman-based prediction and control,”10.1007/s00332-022-09862-1__Journal of Nonlinear Science, vol. 33, Art. no. 14, 2023, doi:10.1007/s00332-022-09862-1 .
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S. S. Hotegni, S. Peitz, and M. B. Berkemeier, “Multi-Objective Optimization for Sparse Deep Neural Network Training,” arXiv:2308.12243. 2023.
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M. Dellnitz et al., “Efficient time stepping for numerical integration using reinforcement  learning,”10.1137/21M1412682__SIAM Journal on Scientific Computing, vol. 45, no. 2, pp. 9–5, 2023, doi:10.1137/21M1412682 .
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