Graph Attention Networks (GAT) | GNN Paper Explained

In this video, I do a deep dive into the graph attention network paper!

GATs have a lot in common with transformers a reason more to keep an eye out for them!

You'll learn about:
✔️ Basic graph theory
✔️ All the nitty-gritty details behind GAT

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✅ GAT website: https://petar-v.com/GAT/
✅ GAT paper: https://arxiv.org/abs/1710.10903
✅ M. Bronstein's blog: https://towardsdatascience.com/do-we-need-deep-graph-neural-networks-be62d3ec5c59
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⌚️ Timetable:
00:00 - A note on geometric deep learning
00:50 - Graph theory basics
08:35 - Intro to GATs (related work)
10:15 - A detailed explanation of the method
16:20 - A multi-head version of the GAT
19:05 - Visualizations, spatial pooling, GNN depth
21:20 - A recap of GAT properties
23:35 - Receptive field of spatial GNNs
24:35 - Datasets, transductive vs inductive learning
30:35 - Results on transductive/inductive benchmarks
35:30 - Representations visualization (t-SNE)

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#graphs #attention #deeplearning

Видео Graph Attention Networks (GAT) | GNN Paper Explained канала The AI Epiphany