Sorting Data via a Look-Up-Table Neural Network and Self-Regulating Index
Joint Authors
Zhao, Ying
Hu, Dongli
Huang, Dongxia
Liu, You
Yang, Zitong
Mao, Lei
Liu, Chao
Zhou, Fangfang
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-27
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
The so-called learned sorting, which was first proposed by Google, achieves data sorting by predicting the placement positions of unsorted data elements in a sorted sequence based on machine learning models.
Learned sorting pioneers a new generation of sorting algorithms and shows a great potential because of a theoretical time complexity ON and easy access to hardware-driven accelerating approaches.
However, learned sorting has two problems: controlling the monotonicity and boundedness of the predicted placement positions and dealing with placement conflicts of repetitive elements.
In this paper, a new learned sorting algorithm named LS is proposed.
We integrate a back propagation neural network with the technique of look-up-table in LS to guarantee the monotonicity and boundedness of the predicted placement positions.
We design a data structure called the self-regulating index in LS to tentatively store and duly update placement positions for eliminating potential placement conflicts.
Results of three controlled experiments demonstrate that LS can effectively control the monotonicity and boundedness, achieve a better time consumption than quick sort and Google’s learned sorting, and present an excellent stability when the data size or the number of repetitive elements increases.
American Psychological Association (APA)
Zhao, Ying& Hu, Dongli& Huang, Dongxia& Liu, You& Yang, Zitong& Mao, Lei…[et al.]. 2020. Sorting Data via a Look-Up-Table Neural Network and Self-Regulating Index. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1142093
Modern Language Association (MLA)
Zhao, Ying…[et al.]. Sorting Data via a Look-Up-Table Neural Network and Self-Regulating Index. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1142093
American Medical Association (AMA)
Zhao, Ying& Hu, Dongli& Huang, Dongxia& Liu, You& Yang, Zitong& Mao, Lei…[et al.]. Sorting Data via a Look-Up-Table Neural Network and Self-Regulating Index. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1142093
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1142093