Using Deep Learning on Non-Euclidean Geometries

This is a fascinating interview with Max Welling that is a deep dive into geometric deep learning.  What that means is using deep learning on non-euclidean data. So graph nets would be one example of this.  But in this particular case we're talking about extending this to gauge or SE(30) equivariance.

And in fact Max and his students have published papers where they look at how a deep learning network would work on a quantum computer, and then modify it so that that quantum computer network can run on conventional deep learning hardware.

Other topics covered include whether protein folding results from deep mind used a SE (3) transformer, GPT-3,  the whole notion of building prior knowledge of your data into a neural net, bias variance tradeoffs associated with that, probabilistic numeric convolutional networks, chaos theory, etc.

Definitely brain expanding, so let's check it out.

Here are some papers to check out associated with the above discussion

Probabilistic Numeric Convolutional Neural Networks - here

Quantum Deformed Neural Networks - here

SE(3) - Equivariant Convolutions - here


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