Statistical theory for image classification using deep convolutional
neural networks with cross-entropy loss under the hierarchical max-pooling
model

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Statistical theory for image classification using deep convolutional neural networks with cross-entropy loss under the hierarchical max-pooling model

27 November 2020

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Papers citing "Statistical theory for image classification using deep convolutional neural networks with cross-entropy loss under the hierarchical max-pooling model"

10 / 10 papers shown

Title
Misclassification bounds for PAC-Bayesian sparse deep learning The Tien Mai UQCV BDL 174 5 0 02 May 2024
On the rates of convergence for learning with convolutional neural networks Yunfei Yang Han Feng Ding-Xuan Zhou 204 3 0 25 Mar 2024
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes Hyunouk Ko Xiaoming Huo 134 1 0 08 Jan 2024
Optimal Convergence Rates of Deep Neural Networks in a Classification Setting Josephine T. Meyer 82 2 0 25 Jul 2022
Minimax Optimal Deep Neural Network Classifiers Under Smooth Decision Boundary Tianyang Hu Ruiqi Liu Zuofeng Shang Guang Cheng 75 3 0 04 Jul 2022
Analysis of convolutional neural network image classifiers in a rotationally symmetric model Michael Kohler Benjamin Kohler 85 6 0 11 May 2022
Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks Hao Liu Minshuo Chen T. Zhao Wenjing Liao 96 35 0 07 Sep 2021
Approximation Properties of Deep ReLU CNNs Juncai He Lin Li Jinchao Xu 184 21 0 01 Sep 2021
Convergence rates of deep ReLU networks for multiclass classification Thijs Bos Johannes Schmidt-Hieber 112 24 0 02 Aug 2021
Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling Benjamin Walter FAtt 67 17 0 31 May 2021