Deep Learning - Graph Deep Learning Part 2

Deep Learning - Graph Deep Learning Part 2 In this video, we demonstrate how to go from spectral to spatial domain in graphs.   Further Reading: A gentle Introduction to Deep Learning References [1]: Kipf, Thomas N., and Max Welling. "Semi-supervised classification with graph convolutional networks." arXiv preprint arXiv:1609.02907 (2016). [2]: Hamilton, Will, Zhitao Ying, and Jure Leskovec. "Inductive representation learning on large graphs." Advances in neural information processing systems. 2017. [3]: Wolterink, Jelmer M., Tim Leiner, and Ivana Išgum. "Graph convolutional networks for coronary artery segmentation in cardiac CT angiography." International Workshop on Graph Learning in Medical Imaging. Springer, Cham, 2019. [4]: Wu, Zonghan, et al. "A comprehensive survey on graph neural networks." arXiv preprint arXiv:1901.00596 (2019). [5]: Bronstein, Michael et al. Lecture “Geometric deep learning on graphs and manifolds” held at SIAM Tutorial Portlan (2018) Image References [a] https://de.serlo.org/mathe/funktionen/funktionsbegriff/funktionen-graphen/graph-funktion [b] https://www.nwrfc.noaa.gov/snow/plot_SWE.php?id=AFSW1 [c] https://tennisbeiolympia.wordpress.com/meilensteine/steffi-graf/ [d] https://www.pinterest.de/pin/624381935818627852/ [e] https://www.uihere.com/free-cliparts/the-pentagon-pentagram-symbol-regular-polygon-golden-five-pointed-star-2282605 [f] http://geometricdeeplearning.com/ (Geometric Deep Learning on Graphs and Manifolds) [g] https://i.stack.imgur.com/NU7y2.png [h] https://de.wikipedia.org/wiki/Datei:Convolution_Animation_(Gaussian).gif [i]https://www.researchgate.net/publication/306293638/figure/fig1/AS:396934507450372@1471647969381/Example-of-centerline- extracted-left-and-coronary-artery-tree-mesh-reconstruction.png [j] https://www.eurorad.org/sites/default/files/styles/figure_image_teaser_large/public/figure_image/2018-08/0000015888/000006.jpg?itok=hwX1sbCO