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Chen, J., Zhou, D., Tang, Y., Yang, Z., Cao, Y. and Gu, Q. (2020) Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, 11-17 July 2020, 3267-3275.
https://doi.org/10.24963/ijcai.2020/452

has been cited by the following article:

• TITLE: Theory of Structural Learning

  AUTHORS: Dong H. Liu

  KEYWORDS: Structural Learning, Central Diagram Processor, Software Diagram, Machine Life

  JOURNAL NAME: Intelligent Control and Automation, Vol.17 No.1, December 30, 2025

  ABSTRACT: This is the first paper to be written on the theory of structural learning. The first section outlines the overall concept; the second section proposes the logical universe as the environment for structural learning; the third, fourth and fifth sections detail the theories of the Central Diagram Processor, Software Diagram and Diagram Thinking, with the Central Diagram Processor acting as the machine’s brain, the Software Diagram as its body, and the Diagram Thinking as its life activities; finally, the sixth section explains existing research achievements in machine life and structural learning. The main purpose of this paper is to recommend the idea of Structural Learning for Machine Life to human society.