#71 - ZAK JOST (Graph Neural Networks + Geometric DL) [UNPLUGGED]
Machine Learning Street Talk (MLST) - A podcast by Machine Learning Street Talk (MLST)
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Special discount link for Zak's GNN course - https://bit.ly/3uqmYVq Patreon: https://www.patreon.com/mlst Discord: https://discord.gg/ESrGqhf5CB YT version: https://youtu.be/jAGIuobLp60 (there are lots of helper graphics there, recommended if poss) Want to sponsor MLST!? Let us know on Linkedin / Twitter. [00:00:00] Preamble [00:03:12] Geometric deep learning [00:10:04] Message passing [00:20:42] Top down vs bottom up [00:24:59] All NN architectures are different forms of information diffusion processes (squashing and smoothing problem) [00:29:51] Graph rewiring [00:31:38] Back to information diffusion [00:42:43] Transformers vs GNNs [00:47:10] Equivariant subgraph aggregation networks + WL test [00:55:36] Do equivariant layers aggregate too? [00:57:49] Zak's GNN course Exhaustive list of references on the YT show URL (https://youtu.be/jAGIuobLp60)
