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A fundamental problem in machine learning is to understand how neural networks make accurate predictions, while seemingly bypassing the curse of dimensionality.
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Neural networks can learn representations with gradient descent
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Flat minima generalize for low-rank matrix recovery
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A rewriting system for convex optimization problems
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A convergence analysis of gradient descent for deep linear neural networks
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Bottleneck structure in learned features: Low-dimension vs regularity tradeoff
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Implicit bias of large depth networks: A notion of rank for nonlinear functions
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Neural networks efficiently learn low-dimensional representations with sgd
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Linear neural network layers promote learning single-and multiple-index models
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Compressed sensing — Wikipedia, the free encyclopedia, 2023
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Nonlinear dimensionality reduction — Wikipedia, the free encyclopedia, 2023
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