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Found 3 papers, 2 papers with code
Characterizing the Spectrum of the NTK via a Power Series Expansion
We provide expressions for the coefficients of this power series which depend on both the Hermite coefficients of the activation function as well as the depth of the network.
Learning curves for Gaussian process regression with power-law priors and targets
We characterize the power-law asymptotics of learning curves for Gaussian process regression (GPR) under the assumption that the eigenspectrum of the prior and the eigenexpansion coefficients of the target function follow a power law.
Implicit Bias of Gradient Descent for Mean Squared Error Regression with Two-Layer Wide Neural Networks
\hj{For stochastic gradient descent we obtain the same implicit bias result.}
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