论文标题
通过消除路径依赖性来构建基础路径
Constructing Basis Path Set by Eliminating Path Dependency
论文作者
论文摘要
基础路径集中在神经网络中的工作方式仍然神秘，并且妨碍了新出现的G-SGD算法对更实用的网络的概括。从图理论的角度来提出基本路径集搜索问题，以找到常规复杂的神经网络中的基础路径。我们的论文旨在发现两个独立子结构之间的路径依赖性的根本原因。算法DEAH旨在通过消除这种路径依赖性来解决基本路径集搜索问题。提出了路径细分链有效地消除链内和链之间的路径依赖性。提供了多项式时间复杂性的理论证明和分析。因此，本文提供了一种方法，可以在更一般的神经网络中找到基础路径，该网络在更实际的情况下为G-SGD算法提供了理论和算法支持。
The way the basis path set works in neural network remains mysterious, and the generalization of newly appeared G-SGD algorithm to more practical network is hindered. The Basis Path Set Searching problem is formulated from the perspective of graph theory, to find the basis path set in a regular complicated neural network. Our paper aims to discover the underlying cause of path dependency between two independent substructures. Algorithm DEAH is designed to solve the Basis Path Set Searching problem by eliminating such path dependency. The path subdivision chain is proposed to effectively eliminate the path dependency inside the chain and between chains. The theoretical proofs and analysis of polynomial time complexity are presented. The paper therefore provides one methodology to find the basis path set in a more general neural network, which offers theoretical and algorithmic support for the application of G-SGD algorithm in more practical scenarios.
