May 14, 2024, 4:43 a.m. | Michael Kohler, Adam Krzyzak, Benjamin Walter

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.07619v1 Announce Type: cross
Abstract: 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.

abstract analysis arxiv classification classifier convergence convolutional convolutional neural network convolutional neural networks cs.lg difference global gradient image layer network networks neural network neural networks pooling rate stat.ml type

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Quality Intern

@ Syngenta Group | Toronto, Ontario, Canada