In an anisotropic random-matrix model of gradient flow, the teacher signal produces a transient BBP transition where the outlier eigenvalue emerges only in an intermediate time window before overfitting.
Disordered dynamics in high dimensions: Connections to random matrices and machine learning
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A two-level DMFT tracks bulk and outlier spectral dynamics in wide networks, predicting width-consistent outlier growth and hyperparameter transfer under muP scaling for deep linear nets while noting bulk restructuring for large-output tasks.
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Random Matrix Theory of Early-Stopped Gradient Flow: A Transient BBP Scenario
In an anisotropic random-matrix model of gradient flow, the teacher signal produces a transient BBP transition where the outlier eigenvalue emerges only in an intermediate time window before overfitting.
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Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer
A two-level DMFT tracks bulk and outlier spectral dynamics in wide networks, predicting width-consistent outlier growth and hyperparameter transfer under muP scaling for deep linear nets while noting bulk restructuring for large-output tasks.
- Linear equivalence of nonlinear recurrent neural networks