During the last few years, significant progress has been made in the theoretical understanding of deep networks. We review our contributions in the areas of approximation theory and optimization. We also introduce a new approach based on cross‐validation leave‐one‐out stability to estimate bounds on the expected error of overparametrized classifiers, such as deep networks. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

中文翻译：

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深度网络理论中的一些问题概述

在过去的几年中，对深度网络的理论理解取得了重大进展。我们回顾了我们在近似理论和优化领域的贡献。我们还引入了一种基于交叉验证留一法稳定性的新方法，以估计过度参数化分类器（例如深度网络）的预期误差的范围。©2020日本电气工程师学会。由Wiley Periodicals LLC发布。