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.
Distribution of eigenvalues for some sets of random matrices.Mathematics of the USSR-Sbornik, 1(4):457–483
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SMoA is a new PEFT adapter that uses block-wise Hadamard-modulated low-rank branches on spectral partitions to cover more pretrained spectral directions than standard LoRA under a smaller parameter budget.
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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.
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SMoA: Spectrum Modulation Adapter for Parameter-Efficient Fine-Tuning
SMoA is a new PEFT adapter that uses block-wise Hadamard-modulated low-rank branches on spectral partitions to cover more pretrained spectral directions than standard LoRA under a smaller parameter budget.