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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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In this repo you can find the code used in the experiments that were presented in our paper: "Local, Global and scaledependent node roles".
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Computational Network Science / 2021  Outlier Detection for Trajectories via Flowembeddings
MIT LicenseF. 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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"Improving the visibility of minorities through network growth interventions", Communication Physics 6, 108 (2023), doi:10.1038/s42005023012189
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Computational Network Science / 2023  An Optimizationbased Approach To Node Role Discovery in Networks
MIT License"An Optimizationbased Approach To Node Role Discovery in Networks: Approximating Equitable Partitions", Michael Scholkemper and Michael T. Schaub, (2023), NeurIPS23.
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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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Computational Network Science / 2023  Representing Edge Flows on Graphs via Sparse Cell Complexes
MIT License"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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Institute of Aerodynamics and Chair of Fluid Mechanics / 2DMEMD
GNU General Public License v3.0 onlyUpdated 
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5GCOMET / 5GFINS
GNU Affero General Public License v3.0Graphical Indoor Network Simulator for factory environments. Developed as part of the project 5GCOMET.
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