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Repository for all things concerning the RWTH course "Object Oriented Software Construction" in the winter semester of 2019.
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This repository describes the ways in which you can contribute your Code to Coscine
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In this example, you can investigate how a neural network can fit an arbitrary function during the training iterations.
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Codebeispiele zur Vorlesung Einführung in die Programmierung I Wintesemester 2021/22 TU Darmstadt
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M. Scholkemper and M. T. Schaub, "Local, Global And ScaleDependent Node Roles," 2021 IEEE International Conference on Autonomous Systems (ICAS), Montreal, QC, Canada, 2021, pp. 15, doi: 10.1109/ICAS49788.2021.9551110.
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F. Frantzen, J.B. Seby and M. T. Schaub, "Outlier Detection for Trajectories via Flowembeddings," 2021 55th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA, 2021, pp. 15681572, doi: 10.1109/IEEECONF53345.2021.9723128.
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M. Scholkemper and M. T. Schaub, "Blind Extraction of Equitable Partitions from Graph Signals," ICASSP 2022  2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, Singapore, 2022, pp. 58325836, doi: 10.1109/ICASSP43922.2022.9746676.
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"Improving the visibility of minorities through network growth interventions", Communication Physics 6, 108 (2023), doi:10.1038/s42005023012189
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"An Optimizationbased Approach To Node Role Discovery in Networks: Approximating Equitable Partitions", Michael Scholkemper and Michael T. Schaub, (2023), NeurIPS23.
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Javier Esparza, Vincent P. Grande: "BlackBox Testing Liveness Properties of Partially Observable Stochastic Systems", ICALP 2023. 50th EATS International Colloquium on Automata, Languages and Programming, 2023
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Stamm FI, Scholkemper M, Strohmaier M, Schaub MT. Neighborhood structure configuration models. InProceedings of the ACM Web Conference 2023 2023 Apr 30 (pp. 210220).
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"Nonisotropic Persistent Homology: Leveraging the Metric Dependency of PH", Vincent P. Grande and Michael T. Schaub, Proceedings of the 2nd Annual Learning on Graphs Conference.
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"Representing Edge Flows on Graphs via Sparse Cell Complexes", Learning on Graphs (LoG) 2023; see also https://github.com/josefhoppe/edgeflowcellcomplexes
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