Equivariant Neural Networks | Part 3/3 - Transformers and GNNs

[@IMdigitalworks]

Hey, thank you for the video. Do you know some resources about SE(3) invariant gnns for points clouds? That is, I would like a gnn to encode local geometries of point neighborhoods that be would invariant under SE(3) (not equivariant).

[@chenweilong2505]

Incredible video. Awesome! Thanks!

[@v_pryadchenko]

Where could I find links to parts 1 and 2?

[@julienblanchon6082]

Part1: kzbin.info/www/bejne/aJOzkH6rd9eLicU Part2: kzbin.info/www/bejne/qGHbqqubaJaEnbc

[@faiazahsan6774]

Your GNN video serise is really helpful for a beginer like me. Thank you very much. p.s: Any chance you can make a project video on Supply Chain Risk Detection using GNN?

[@prajwol_poudel]

Do you have a background in mathematics? coming from a non-mathematics, non-computer engineering/ CS background, the mathematics I've learned so far feels like cave-man mathematics when seeing all the fancy mathematics in the video, can't even imagine trying to read any of the paper on my own. How can someone without a good background in mathematics navigate/dive into such topics which demands high mathematical rigour?

[@DeepFindr]

Hi! Not directly mathematics, but computer science. Yeah I agree I doesn't look so trivial but I just read different articles about it until I mostly understood it. But I'm far from being an expert and there are still many nuances I didn't fully understand. Took me ages however ;-) This area is certainly one of the math heavier ones. I think given enough time it is possible to understand such papers, but the question is if it's really worth to invest it. The next videos will be more comprehensible again :)

[@zeyutang2084]

@@DeepFindr Hi thanks for the amazing series! I am from an engineering background, just wondering if you could recommend any books or lectures for group theory since I would like to develop a more rigorous mathematical understanding of the topic. Many thanks

[@IgorItkin]

Thanks man!

[@tilkesh]

Thx

[@v_pryadchenko]

Cool!