中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 先进控制与自动化团队
Accelerating Minibatch Stochastic Gradient Descent Using Typicality Sampling

文献类型：期刊论文

作者Peng, Xinyu2; Li, Li1; Wang, Fei-Yue3
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2020-11-01
卷号31期号:11页码:4649-4659
关键词Training Convergence Approximation algorithms Stochastic processes Estimation Optimization Acceleration Batch selection machine learning minibatch stochastic gradient descent (SGD) speed of convergence
ISSN号2162-237X
DOI10.1109/TNNLS.2019.2957003
通讯作者Li, Li(li-li@tsinghua.edu.cn)
英文摘要Machine learning, especially deep neural networks, has developed rapidly in fields, including computer vision, speech recognition, and reinforcement learning. Although minibatch stochastic gradient descent (SGD) is one of the most popular stochastic optimization methods for training deep networks, it shows a slow convergence rate due to the large noise in the gradient approximation. In this article, we attempt to remedy this problem by building a more efficient batch selection method based on typicality sampling, which reduces the error of gradient estimation in conventional minibatch SGD. We analyze the convergence rate of the resulting typical batch SGD algorithm and compare the convergence properties between the minibatch SGD and the algorithm. Experimental results demonstrate that our batch selection scheme works well and more complex minibatch SGD variants can benefit from the proposed batch selection strategy.
资助项目National Key Research and Development Program of China[2018AAA0101400] ; National Natural Science Foundation of China[91720000] ; Beijing Municipal Science and Technology Commission[Z181100008918007]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000587699700019
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Beijing Municipal Science and Technology Commission
源URL[http://ir.ia.ac.cn/handle/173211/41732]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Li, Li
作者单位1.Tsinghua Univ, Dept Automat, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China
2.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100080, Peoples R China
推荐引用方式
GB/T 7714		 Peng, Xinyu,Li, Li,Wang, Fei-Yue. Accelerating Minibatch Stochastic Gradient Descent Using Typicality Sampling[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2020,31(11):4649-4659.
APA Peng, Xinyu,Li, Li,&Wang, Fei-Yue.(2020).Accelerating Minibatch Stochastic Gradient Descent Using Typicality Sampling.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,31(11),4649-4659.
MLA Peng, Xinyu,et al."Accelerating Minibatch Stochastic Gradient Descent Using Typicality Sampling".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 31.11(2020):4649-4659.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。