Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent

Adam Krzyzak; Benjamin Walter; Michael Kohler

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Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent

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arxiv 2405.07619 v1 pith:6K4WTGK5 submitted 2024-05-13 stat.ML cs.LG

Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent

Michael Kohler , Adam Krzyzak , Benjamin Walter This is my paper

classification stat.ML cs.LG

keywords convolutionalnetworkneuralconvergencedescentgradientimagelearned

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Image classification based on over-parametrized convolutional neural networks with a global average-pooling layer is considered. The weights of the network are learned by gradient descent. A bound on the rate of convergence of the difference between the misclassification risk of the newly introduced convolutional neural network estimate and the minimal possible value is derived.

Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent

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