Deep Neural Network Approximation For Hölder Functions
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In this work, we explore the approximation capability of deep Rectified Quadratic Unit neural networks for Hölder-regular functions, with respect to the uniform norm. We find that theoretical approximation heavily depends on the selected activation function in the neural network.
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