The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Minimal gated unit for recurrent neural networks
Open accessAbstract
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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Cites
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Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey
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Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review
Shuvo, M. M. H., Islam, S. K., Cheng, J., & Morshed, B. I. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3226481 · Cited by 436
“The minimal gated unit (MGU) integrates the reset gate and update gate of the GRU [87].”
BackgroundA survey on deep learning-based fine-grained object classification and semantic segmentation
Zhao, B., Feng, J., Wu, X., & Yan, S. (2017) · International Journal of Automation and Computing · doi:10.1007/s11633-017-1053-3 · Cited by 339
Investigating topological order using recurrent neural networks
Hibat-Allah, M., Melko, R. G., & Carrasquilla, J. (2023) · Physical Review B · doi:10.1103/physrevb.108.075152 · Cited by 27
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Metadata
- Authors
- Zhou, G.-B., Wu, J., Zhang, C.-L., & Zhou, Z.-H.
- Venue
- International Journal of Automation and Computing
- Published
- 2016-06
- Volume
- 13
- Issue
- 3
- Pages
- 226-234
- ISSN
- 1476-8186 (ISSN-L)
- 1751-8520
- OpenAlex
- W2319453305
- Cited by
- 357
- References
- 31
- FWCI
- 22.41
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhou, G.-B., Wu, J., Zhang, C.-L., & Zhou, Z.-H. (2016). Minimal gated unit for recurrent neural networks. International Journal of Automation and Computing. https://doi.org/10.1007/s11633-016-1006-2
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