Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent
<|reference_start|>Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent: 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.<|reference_end|>
@article{kohler2024analysis,
    title={Analysis of the rate of convergence of an over-parametrized
  convolutional neural network image classifier learned by gradient descent},
    author={Michael Kohler, Adam Krzyzak, Benjamin Walter},
    journal={arXiv preprint arXiv:2405.07619},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07619},
    primaryClass={stat.ML cs.LG}
}