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Learning threshold neurons via the "edge of stability"
For these models, we provably establish the edge of stability phenomenon and discover a sharp phase transition for the step size below which the neural network fails to learn "threshold-like" neurons (i. e., neurons with a non-zero first-layer bias).
Riemannian Perspective on Matrix Factorization
We study the non-convex matrix factorization approach to matrix completion via Riemannian geometry.
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