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This lecture explains how traditional convolutional neural networks extend to graph data. It covers spectral and spatial Graph Convolutional Networks, including isotropic and anisotropic architectures, comparisons, experiments, and benchmarks.
Syllabus
– Week 13 – Lecture
– Architecture of Traditional ConvNets
– Convolution of Traditional ConvNets
– Spectral Convolution
– Spectral GCNs
– Template Matching, Isotropic GCNs and Benchmarking GNNs
– Anisotropic GCNs and Conclusion
Taught by
Alfredo Canziani