SGD on neural network weights induces a BBP phase transition that detaches signal eigenvalues from the random bulk, yielding an analytically solvable phase diagram for trainability in a linear teacher-student model.
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Infinite-width MLPs implement a nearest-class-mean prototype classifier as their leading-order decision rule under heavy attribute noise, explaining observed robustness in experiments.
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Spectral phase transitions and trainability in neural network learning dynamics
SGD on neural network weights induces a BBP phase transition that detaches signal eigenvalues from the random bulk, yielding an analytically solvable phase diagram for trainability in a linear teacher-student model.
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Learning from almost nothing: How neural networks survive heavy input corruption
Infinite-width MLPs implement a nearest-class-mean prototype classifier as their leading-order decision rule under heavy attribute noise, explaining observed robustness in experiments.