# Characterizing the Spectrum of the NTK via a Power Series Expansion

@article{Murray2022CharacterizingTS, title={Characterizing the Spectrum of the NTK via a Power Series Expansion}, author={Michael Murray and Hui Jin and Benjamin Bowman and Guido Mont{\'u}far}, journal={ArXiv}, year={2022}, volume={abs/2211.07844} }

Under mild conditions on the network initialization we derive a power series expansion for the Neural Tangent Kernel (NTK) of arbitrarily deep feedforward networks in the infinite width limit. 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. We observe faster decay of the Hermite coefficients leads to faster decay in the NTK coefficients and explore the role of depth…

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