NYU Deep Learning Week 13 – Lecture: Graph Convolutional Networks (GCNs) | Xavier Bresson

Join the channel membership: https://www.youtube.com/c/AIPursuit/join Subscribe to the channel: https://www.youtube.com/c/AIPursuit?sub_confirmation=1 Support and Donation: Paypal ⇢ https://paypal.me/tayhengee Patreon ⇢ https://www.patreon.com/hengee BTC ⇢ bc1q2r7eymlf20576alvcmryn28tgrvxqw5r30cmpu ETH ⇢ 0x58c4bD4244686F3b4e636EfeBD159258A5513744 Doge ⇢ DSGNbzuS1s6x81ZSbSHHV5uGDxJXePeyKy Wanted to own BTC, ETH, or even Dogecoin? Kickstart your crypto portfolio with the largest crypto market Binance with my affiliate link: https://accounts.binance.com/en/register?ref=27700065 The video was published under the license of the Creative Commons Attribution license (reuse allowed). It is reposted for educational purposes and encourages involvement in the field of research. Source: https://youtu.be/Iiv9R6BjxHM Subscribe to Alfredo Canziani: https://www.youtube.com/channel/UCupQLyNchb9-2Z5lmUOIijw 0:00:00 – Week 13 – Lecture LECTURE Part A: http://bit.ly/pDL-en-13-1 In this section, we discuss the architecture and convolution of traditional convolutional neural networks. Then we extend to the graph domain. We understand the characteristics of graph and define the graph convolution. Finally, we introduce spectral graph convolutional neural networks and discuss how to perform spectral convolution. 0:00:50 – Architecture of Traditional ConvNets 0:13:11 – Convolution of Traditional ConvNets 0:25:29 – Spectral Convolution LECTURE Part B: http://bit.ly/pDL-en-13-2 This section covers the complete spectrum of Graph Convolutional Networks (GCNs), starting with the implementation of Spectral Convolution through Spectral Networks. It then provides insights on applicability of the other convolutional definition of Template Matching to graphs, leading to Spatial networks. Various architectures employing the two approaches are detailed out with their corresponding pros & cons, experiments, benchmarks and applications. 0:44:30 – Spectral GCNs 1:06:04 – Template Matching, Isotropic GCNs and Benchmarking GNNs 1:33:06 – Anisotropic GCNs and Conclusion