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Exploring the development and application of LSTM variants
- 1 Southwest Jiaotong University
* Author to whom correspondence should be addressed.
Abstract
Long Short-Term Memory (LSTM) is receiving increasing attention as the development of deep learning technology. The gate structure of LSTM enhances long-term memory, forming its superior capacity to complete tasks that challenge traditional RNN. However, considering the wide variety of applications, a comprehensive understanding of the development and application of the model, which is vital for future research, is comparatively lacking. Therefore, this paper is produced with the hope of offering an overview of the development of LSTM. It shows the process of development from RNN to LSTM and explains the aim and necessity of LSTM’s birth. After that it introduces the structure of LSTM, analyses its advantages over RNN, and discusses the application of some popular LSTM variants, such as peephole LSTM, bidirectional LSTM, and GRU. Hopefully, this work can provide a more profound knowledge of LSTM's benefits and potential, identifying worthwhile avenues or fields of future research.
Keywords
Deep Learning, Neural Networks, Long Short-Term Memory (LSTM)
[1]. Hochreiter S and Schmidhuber J 1997 J. Neural Comput. 9 8 1735-1780
[2]. Gers F A Schmidhuber J and Cummins F 2000 J. Neural Comput. 12 10 2451-2471
[3]. Staudemeyer R C and Morris E R 2019 J. Prep ArXiv.1909 09586
[4]. Kong W, Dong Z Y, Jia Y, Hill D J, Xu Y and Zhang Y 2019 J. IEEE. T. Smart Grid. 10 1 841–851
[5]. Muhammad P F, Kusumaningrum R and Wibowo A 2021 J. Proc. Comput. Sci. 179 728–735
[6]. Yang T, Wang H, Aziz S, Jiang H and Peng J 2018 C. IEEE. 364-369
[7]. Gupta A, Nayyar A, Arora S and Jain R 2021 C. Inter. Conf. on Advanced Informatics for Computing Research (Singapore) 1393100–112
[8]. Wan Y 2019 C. 2019 Int. Conf. on Artificial Intelligence and Advanced Manufacturing (AIAM) (Dublin, Ireland). 35-38
[9]. Fu R, Zhang Z and Li L 2016 J. Conf. of Chinese Association of Automation (YAC) (Wuhan, China). 324-328
[10]. Yang S, Yu X and Zhou Y 2020 C.Int. Workshop on Electronic Communication and Artificial Intelligence (IWECAI) (Shanghai, China). 98-101
[11]. Akilan T, Wu Q J, Safaei A, Huo J and Yang Y 2020 J. IEEE Transactions on Intelligent Transportation Systems. 21 3 959–971
Cite this article
Chen,N. (2024). Exploring the development and application of LSTM variants. Applied and Computational Engineering,53,103-107.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Signal Processing and Machine Learning
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