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I believe Bronstein is onto something huge here, with massive implications. Illuminating is the best adjective to describe it, as I watched this keynote:

> https://www.youtube.com/watch?v=w6Pw4MOzMuo

Everything clicked into place and I was given a new language to see the world that combined everything together well beyond the way standard DL is taught:

> we do feature extraction using this function that resembles the receptive fields of the visual cortex and then we project the dense feature representation onto multiple other vectors and pass that through stacked non-linearities, and oh by the way we have myriad of different, seemingly disconnected, architectures that we are not sure why they work, but we call it inductive bias.

> https://geometricdeeplearning.com/

That's my main source, along with the papers that lead up to the proto-book, so pretty much Bronstein's work along with related papers found using `connectedpapers.com`. I don't have an appropriate background so I am grinding through abstract algebra, geometric algebra, will then go into geometry and whatever my supervisor suggests I should read. Sure, I would like to have other people to discuss it, but don't expect much just yet.




I agree, this perspective is very interesting and tames the zoo of architectures through mathematical unification. It is indeed exciting!

Good luck with your studies/learning!




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