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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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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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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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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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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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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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Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal, Vincent P. Grande and Michael T. Schaub, International Conference on Acoustics, Speech, and Signal Processing 2024. https://arxiv.org/abs/2311.14427
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"Learning From Simplicial Data Based on Random Walks and 1D Convolutions", Florian Frantzen and Michael T. Schaub, The Twelfth International Conference on Learning Representations, 2024
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"A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings", Scholkemper, M., Kühn, D., Nabbefeld, G., Musall, S., Kampa, B., & Schaub, M. T. (2024)
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Institute of Aerodynamics and Chair of Fluid Mechanics / 2DMEMD
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