Abstract
Recurrent neural networks (RNN) have been very successful in handling sequence data. However, understanding RNN and finding the best practices for RNN learning is a difficult task, partly because there are many competing and complex hidden units, such as the long short-term memory (LSTM) and the gated recurrent unit (GRU). We propose a gated unit for RNN, named as minimal gated unit (MGU), since it only contains one gate, which is a minimal design among all gated hidden units. The design of MGU benefits from evaluation results on LSTM and GRU in the literature. Experiments on various sequence data show that MGU has comparable accuracy with GRU, but has a simpler structure, fewer parameters, and faster training. Hence, MGU is suitable in RNN's applications. Its simple architecture also means that it is easier to evaluate and tune, and in principle it is easier to study MGU's properties theoretically and empirically.
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This work was supported by National Natural Science Foundation of China (Nos. 61422203 and 61333014), and National Key Basic Research Program of China (No. 2014CB340501).
Recommended by Associate Editor Yi Cao
Guo-Bing Zhou received the B. Sc. degree in computer science from Nanjing University, China in 2013. He is currently a postgraduate student in Nanjing University and will receive the M. Sc. degree in July, 2016.
His research interest is machine learning.
ORCID iD: 0000-0001-9779-481X
Jianxin Wu received the Ph.D. degree in computer science from the Georgia Institute of Technology, USA in 2009. He is currently a professor in the Department of Computer Science and Technology at Nanjing University, China. He has served as an area chair for ICCV 2015 and senior PC member for AAAI 2016.
His research interests include computer vision and machine learning.
ORCID iD: 0000-0002-2085-7568
Chen-Lin Zhang is a candidate for the Bachelor's degree in the Department of Computer Science and Technology, Nanjing University, China.
His research interests include computer vision and machine learning.
Zhi-Hua Zhou is a professor, standing deputy director of the National Key Laboratory for Novel Software Technology, and Founding Director of the LAMDA Group at Nanjing University. He is a Fellow of the AAAI, IEEE, IAPR, IET/IEE, CCF, and an ACM Distinguished Scientist.
His research interests include artificial intelligence, machine learning and data mining.
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Zhou, GB., Wu, J., Zhang, CL. et al. Minimal gated unit for recurrent neural networks. Int. J. Autom. Comput. 13, 226–234 (2016). https://doi.org/10.1007/s11633-016-1006-2
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DOI: https://doi.org/10.1007/s11633-016-1006-2