# Neural Networks as Kernel Learners: The Silent Alignment Effect

@article{Atanasov2021NeuralNA, title={Neural Networks as Kernel Learners: The Silent Alignment Effect}, author={Alexander Atanasov and Blake Bordelon and Cengiz Pehlevan}, journal={ArXiv}, year={2021}, volume={abs/2111.00034} }

Neural networks in the lazy training regime converge to kernel machines. Can neural networks in the rich feature learning regime learn a kernel machine with a data-dependent kernel? We demonstrate that this can indeed happen due to a phenomenon we term silent alignment, which requires that the tangent kernel of a network evolves in eigenstructure while small and before the loss appreciably decreases, and grows only in overall scale afterwards. We show that such an effect takes place in…

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