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- W3132350540 abstract "We study the extent to which wide neural networks may be approximated by Gaussian processes when initialized with random weights. It is a well-established fact that as the width of a network goes to infinity, its law converges to that of a Gaussian process. We make this quantitative by establishing explicit convergence rates for the central limit theorem in an infinite-dimensional functional space, metrized with a natural transportation distance. We identify two regimes of interest; when the activation function is polynomial, its degree determines the rate of convergence, while for non-polynomial activations, the rate is governed by the smoothness of the function." @default.
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- W3132350540 date "2021-02-17" @default.
- W3132350540 modified "2023-09-27" @default.
- W3132350540 title "Non-asymptotic approximations of neural networks by Gaussian processes" @default.
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