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Efficient training of neural networks is difficult. Our second Connectionism post introduces Modular Manifolds, a theoretical step toward more stable and performant training by co-designing neural net optimizers with manifold constraints on weight matrices. https://t.co/PGG4zy3u23 We explore a fundamental understanding of the geometry of neural network optimization.