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[1]刘帅师,程曦,郭文燕,等.深度学习方法研究新进展[J].智能系统学报,2016,11(5):567-577.[doi:10.11992/tis.201511028]
　LIU Shuaishi,CHENG Xi,GUO Wenyan,et al.Progress report on new research in deep learning[J].CAAI Transactions on Intelligent Systems,2016,11(5):567-577.[doi:10.11992/tis.201511028]

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深度学习方法研究新进展

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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 11 期数: 2016年第5期页码: 567-577 栏目: 综述出版日期: 2016-11-01

Title:: Progress report on new research in deep learning

作者:: 刘帅师, 程曦, 郭文燕, 陈奇; 长春工业大学电气与电子工程学院, 吉林长春 130000

Author(s):: LIU Shuaishi, CHENG Xi, GUO Wenyan, CHEN Qi; College of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130000, China

关键词:: 深度学习; 卷积神经网络; 深度信念网络; 深度玻尔兹曼机; 堆叠自动编码器

Keywords:: deep learning; convolutional neural network; deep belief networks; deep Boltzmann machine; automatic stacking encoder

分类号:: TP18

DOI:: 10.11992/tis.201511028

摘要:: 本文依据模型结构对深度学习进行了归纳和总结，描述了不同模型的结构和特点。首先介绍了深度学习的概念及意义，然后介绍了4种典型模型：卷积神经网络、深度信念网络、深度玻尔兹曼机和堆叠自动编码器，并对近3年深度学习在语音处理、计算机视觉、自然语言处理以及医疗应用等方面的应用现状进行介绍，最后对现有深度学习模型进行了总结，并且讨论了未来所面临的挑战。

Abstract:: Deep learning has recently received widespread attention. Using a model structure, this paper gives a summarization and analysis on deep learning by describing and reviewing the structure and characteristics of different models. The paper firstly introduces the concept and significance of deep learning, and then reviews four typical models:a convolutional neural network; deep belief networks; the deep Boltzmann machine; and an automatic stacking encoder. The paper then concludes by reviewing the applications of deep learning as regards speech processing, computer vision, natural language processing, medical science, and other aspects. Finally, the existing deep learning model is summarized and future challenges discussed.

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备注/Memo

收稿日期:2015-11-27。
基金项目:吉林省科技厅青年科研基金项目（20140520065JH，20140520076JH）；长春工业大学科学研究发展基金自然科学计划项目（2010XN07）.
作者简介:刘帅师,女,1981年生,讲师,博士,主要研究方向为模式识别、计算机视觉;程曦,男,1989年生,硕士研究生,主要研究方向为模式识别、机器学习;郭文燕,女,1991年生,硕士研究生,主要研究方向为模式识别、机器学习。
通讯作者:刘帅师.E-mail:liu-shuaishi@126.com

更新日期/Last Update: 1900-01-01

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深度学习方法研究新进展

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