Facebook Pixel
Machine Learning Street Talk (MLST)

#71 - ZAK JOST (Graph Neural Networks + Geometric DL) [UNPLUGGED]

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)

Machine Learning Street Talk (MLST)
Not playing