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yeah this was my main problem, I guess he is technically right because they are geometric but many of his analogies like the paper crumpling were deeply misleading as they would imply that the transformations are linear. The fact that they are not is fundamental to neural networks working.



Paper-crumpling is nonlinear. Maybe your complaint is rather that paper-crumpling is "only" a topology-preserving diffeomorphism?




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