A SOM-based Gradient-Free Deep Learning Method with Convergence Analysis
Machine Learning
2022-01-27 v2
Abstract
As gradient descent method in deep learning causes a series of questions, this paper proposes a novel gradient-free deep learning structure. By adding a new module into traditional Self-Organizing Map and introducing residual into the map, a Deep Valued Self-Organizing Map network is constructed. And analysis about the convergence performance of such a deep Valued Self-Organizing Map network is proved in this paper, which gives an inequality about the designed parameters with the dimension of inputs and the loss of prediction.
Cite
@article{arxiv.2101.05612,
title = {A SOM-based Gradient-Free Deep Learning Method with Convergence Analysis},
author = {Shaosheng Xu and Jinde Cao and Yichao Cao and Tong Wang},
journal= {arXiv preprint arXiv:2101.05612},
year = {2022}
}
Comments
there is some little typing err in parameter setting part, which may mislead readers