A method that alternates gradient steps on a neural network backbone with closed-form optimal updates to the final linear layer under squared loss, including an SGD adaptation and NTK-regime convergence analysis.
[BBSS22] Alberto Bietti, Joan Bruna, Clayton Sanford, and Min Jae Song
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2025 2verdicts
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The paper derives sharp matching convergence rates for spectral methods in linear regression via feature space decomposition, enabling pre-ordering of algorithms and generalizing saturation effects.
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Closed-Form Last Layer Optimization
A method that alternates gradient steps on a neural network backbone with closed-form optimal updates to the final linear layer under squared loss, including an SGD adaptation and NTK-regime convergence analysis.
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Sharp convergence rates for Spectral methods via the feature space decomposition method
The paper derives sharp matching convergence rates for spectral methods in linear regression via feature space decomposition, enabling pre-ordering of algorithms and generalizing saturation effects.