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深度学习认知计算综述

陈伟宏 安吉尧 李仁发 李万里

陈伟宏, 安吉尧, 李仁发, 李万里. 深度学习认知计算综述. 自动化学报, 2017, 43(11): 1886-1897. doi: 10.16383/j.aas.2017.c160690
引用本文: 陈伟宏, 安吉尧, 李仁发, 李万里. 深度学习认知计算综述. 自动化学报, 2017, 43(11): 1886-1897. doi: 10.16383/j.aas.2017.c160690
CHEN Wei-Hong, AN Ji-Yao, LI Ren-Fa, LI Wan-Li. Review on Deep-learning-based Cognitive Computing. ACTA AUTOMATICA SINICA, 2017, 43(11): 1886-1897. doi: 10.16383/j.aas.2017.c160690
Citation: CHEN Wei-Hong, AN Ji-Yao, LI Ren-Fa, LI Wan-Li. Review on Deep-learning-based Cognitive Computing. ACTA AUTOMATICA SINICA, 2017, 43(11): 1886-1897. doi: 10.16383/j.aas.2017.c160690

深度学习认知计算综述

doi: 10.16383/j.aas.2017.c160690
基金项目: 

国家自然科学基金 61672217

国家自然科学基金 61370097

详细信息
    作者简介:

    陈伟宏 湖南大学信息科学与工程学院博士研究生, 湖南城市学院教授.2006年获得湖南大学硕士学位.主要研究方向为信息物理系统, 分布式计算, 机器学习.E-mail:whchen@hnu.edu.cn

    李仁发 湖南大学教授, 华中科技大学博士.主要研究方向为嵌入式系统, 信息物理系统, 人工智能与机器视觉.E-mail:lirenfa@hnu.edu.cn

    李万里 湖南大学信息科学与工程学院博士研究生.2014年获得湖南大学学士学位.主要研究方向为机器学习, 计算机视觉, 智能交通系统和驾驶员行为分析.E-mail:liwanli@hnu.edu.cn

    通讯作者:

    安吉尧 湖南大学教授.2012年获得湖南大学博士学位.主要研究方向为信息物理系统, 并行与分布式计算, 计算智能.本文通信作者.E-mail:anbobcn@aliyun.com

Review on Deep-learning-based Cognitive Computing

Funds: 

National Natural Science Foundation of China 61672217

National Natural Science Foundation of China 61370097

More Information
    Author Bio:

    Ph. D. candidate at the College of Computer Science and Electronic Engineering, Hunan University, and professor at Hunan City University. She received her master degree from Hunan University in 2006. Her research interest covers cyber physical systems, distributed computing, and machine learning

    Professor at Hunan University. He received his Ph. D. degree from Huazhong University of Science and Technology. His research interest covers embedded system, cyber-physical systems, artificial intelligence, and machine vision

    Ph. D. candidate at the College of Computer Science and Electronic Engineering, Hunan University. He received his bachelor degree from Hunan University in 2014. His research interest covers machine learning, computer vision, intelligent transportation systems, and driver behavior analysis

    Corresponding author: AN Ji-Yao Professor at Hunan University. He received his Ph. D. degree from Hunan University in 2012. His research interest covers cyber-physical systems, parallel and distributed computing, and computing intelligence. Corresponding author of this paper
  • 摘要: 随着大数据和智能时代的到来,机器学习的研究重心已开始从感知领域转移到认知计算(Cognitive computing,CC)领域,如何提升对大规模数据的认知能力已成为智能科学与技术的一大研究热点,最近的深度学习有望开启大数据认知计算领域的研究新热潮.本文总结了近年来大数据环境下基于深度学习的认知计算研究进展,分别从深度学习数据表示、认知模型、深度学习并行计算及其应用等方面进行了前沿概况、比较和分析,对面向大数据的深度学习认知计算的挑战和发展趋势进行了总结、思考与展望.
    1)  本文责任编委 张化光
  • 图  1  深度学习认知计算示意图

    Fig.  1  Diagram of deep learning cognitive computing

    图  2  基于张量的数据表示和处理框架

    Fig.  2  Tensor-based data representation and processing framework

    图  3  深度学习一般模型

    Fig.  3  The deep learning cognitive model

    图  4  深度卷积网模型LeNet

    Fig.  4  Deep convolution network model of LeNet

    图  5  自编码器结构

    Fig.  5  The structure of the auto-encoder

    图  6  RNN结构

    Fig.  6  The structure of RNN

    图  7  基于GPU的数据并行和模型并行混合架构

    Fig.  7  The hybrid architecture based on data parallel and model parallel

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出版历程
  • 收稿日期:  2016-10-10
  • 录用日期:  2017-06-22
  • 刊出日期:  2017-11-20

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