Can Deep Learning Only Be Neural Networks?

被引：0

作者：

Zhou, Zhi-Hua ^{[1
]}

机构：

[1] Nanjing Univ, Natl Key Lab Novel Software Technol, Nanjing 210093, Peoples R China

来源：

PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING (WSDM '20) | 2020年

关键词：

Machine Learning; Deep Learning; Deep Forest; Ensemble Learning;

D O I：

10.1145/3336191.3372190

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The word "deep learning" is generally regarded as a synonym of "deep neural networks (DNNs)". In this talk, we will discuss on essentials in deep learning and claim that deep learning is not necessarily to be realized by neural networks and differentiable modules. We will then present an exploration to non-NN style deep learning, where the building blocks are non-differentiable modules and the training process does not rely on backpropagation or gradient-based adjustment. We will also talk about some recent advances and challenges in this direction of research.

引用

页码：6 / 6

页数：1