Wie funktioniert Autonomes Fahren?

1. J. Frochte, Maschinelles Lernen - Grundlagen und Algorithmen in Python, 3. Auflage, Carl Hanser Verlag, 2020.
2. P. Wilmott, Machine Learning: An Applied Mathematics Introduction, Panda Ohana Publishing, 2019.
3. M.P. Deisenroth, A.A. Faisal, C.S. Ong, Mathematics for machine learning, Cambridge University Press, 2020.
4. B. Shi, S.S. Iyengar, Mathematical Theories of Machine Learning - Theory and Applications, Springer, Berlin, 2020.
5. R. Searcy, Machine Learning Takes Automotive Radar Further, Aptiv white paper, 2020.
6. Y. Zhou, O. Tuzel, Voxelnet: End-to-end learning for point cloud based 3d object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4490-4499.
7. C.R. Qi, et al., Pointnet: Deep learning on point sets for 3d classification and segmentation, Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 652-660.
8. B.R. Kiran, et al., Deep reinforcement learning for autonomous driving: A survey, IEEE Transactions on Intelligent Transportation Systems, 2021.

Mathematische Architekturen für Neuronale Netze

1. S. Dittmer, T. Kluth, P. Maass, D.O. Baguer, Regularization by architecture: A deep prior approach for inverse problems, Journal of Mathematical Imaging and Vision 62, no. 3 (2020): 456-470.
2. D.O. Baguer, J. Leuschner, M. Schmidt, Computed tomography reconstruction using deep image prior and learned reconstruction methods, Inverse Problems 36, no. 9 (2020): 094004.
3. S. Dittmer, T. Kluth, M.Thorstein, R. Henriksen, P. Maass, Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset, arXiv preprint arXiv:2007.01593 (2020).
4. A. Qayyum, I. Ilahi, F. Shamshad, Untrained Neural Network Priors for Inverse Imaging Problems: A Survey, 2021.
5. P. Cascarano, A. Sebastiani, M. Colomba Comes, Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM, arXiv preprint arXiv:2009.11380 (2020).
6. P. Cascarano, G. Franchini, F. Porta, A. Sebastiani, Solving discrepancy constrained Deep Image Prior with explicit and implicit regularization via ADMM.

Wie soziale Netzwerke bei der Behebung von Softwarefehlern helfen

1. M.S. Zanetti, I. Scholtes, C.J. Tessone, F. Schweitzer, Categorizing bugs with social networks: a case study on four open source software communities, In: 2013 35th International Conference on Software Engineering (ICSE) (pp. 1032-1041). IEEE, 2013.
2. M.S. Zanetti, I. Scholtes, C.J. Tessone, F. Schweitzer, The rise and fall of a central contributor: Dynamics of social organization and performance in the gentoo community, In: 2013 6th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE) (pp. 49-56). IEEE, 2013.
3. A. Schnabel, Entwicklung einer Heuristik für den Testbedarf von Open Source Softwareprojekten auf einer Social Coding Site.

Die Mathematik hinter den Empfehlungen von Netflix und Amazon Prime

1. https://www.netflixprize.com/
2. https://en.wikipedia.org/wiki/Netflix_Prize
3. Y. Koren, The BellKor Solution to the Netflix Grand Prize, (2009).
   https://www.asc.ohio-state.edu/statistics/dmsl/GrandPrize2009_BPC_BellKor.pdf
4. A. Töscher, M. Jahrer, R. Bell, The BigChaos Solution to the Netflix Grand Prize, (2009).
5. M. Piotte, M. Chabbert, The Pragmatic Theory solution to the Netflix Grand Prize, (2009).
6. https://www.wired.com/2009/06/1-million-netflix-prize-so-close-they-can-taste-it/
7. https://www.wired.com/2009/09/bellkors-pragmatic-chaos-wins-1-million-netflix-prize/
8. E.J. Candes and T. Tao, The Power of Convex Relaxation: Near-Optimal Matrix Completion, IEEE Transactions on Information Theory, 56(5) (2010), 2053-2080, doi: 10.1109/TIT.2010.2044061.
9. T. Hastie, R. Mazumder, J.D. Lee, R. Zadeh, Matrix completion and low-rank SVD via fast alternating least squares, The Journal of Machine Learning Research, 16(1) (2015), 3367-3402.

Sprachen durch Zählen von Wörtern bändigen

1. A. Koutsoudas, Mechanical translation and Zipf's law, Language (1957), 545-552.
2. M. Turchi, T. De Bie, C. Goutte, N. Cristianini, Learning to translate: A statistical and computational analysis, Advances in Artificial Intelligence, 2012.
3. I. Kanter, H. Kfir, B. Malkiel, M. Shlesinger, Identifying universals of text translation, Journal of Quantitative Linguistics, 13(01) (2006), 35-43.
4. V.V. Bochkarev, E.Y. Lerner, The Zipf law for random texts with unequal letter probabilities and the Pascal pyramid, Russian Mathematics, 56(12) (2012), 25-27.