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math.ST2025
Learning of deep convolutional network image classifiers via stochastic gradient descent and over-parametrization
Michael Kohler, Adam Krzyzak, Alisha Sänger
Image classification from independent and identically distributed random variables is considered. Image classifiers are defined which are based on a linear combination of deep conv…
math.ST2024
Statistical theory for image classification using deep convolutional neural networks with cross-entropy loss under the hierarchical max-pooling model
Michael Kohler, Sophie Langer
Convolutional neural networks (CNNs) trained with cross-entropy loss have proven to be extremely successful in classifying images. In recent years, much work has been done to also…