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arXiv · 1903.04982

A Capsule-unified Framework of Deep Neural Networks for Graphical Programming

Yujian Li·Chuanhui Shan
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Abstract

Recently, the growth of deep learning has produced a large number of deep neural networks. How to describe these networks unifiedly is becoming an important issue. We first formalize neural networks in a mathematical definition, give their directed graph representations, and prove a generation theorem about the induced networks of connected directed acyclic graphs. Then, using the concept of capsule to extend neural networks, we set up a capsule-unified framework for deep learning, including a mathematical definition of capsules, an induced model for capsule networks and a universal backpropagation algorithm for training them. Finally, we discuss potential applications of the framework to graphical programming with standard graphical symbols of capsules, neurons, and connections.

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Yujian Li, Chuanhui Shan. 2019-03-07. A Capsule-unified Framework of Deep Neural Networks for Graphical Programming. https://arxiv.org/abs/1903.04982

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