The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Gate-variants of Gated Recurrent Unit (GRU) neural networks
Open accessAbstract
The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNNs) by retaining the structure and systematically reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while reducing the computational expense. In this comparative study, we simply refer to the three variants as, respectively, GRU1, GRU2, and GRU3 RNNs.
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
Research works printed but not linked: 10
- gers, 2002, Learning precise timing with lstm recurrent networks, Journal of Machine Learning Research, 3, 115
- salem, 2016, Reduced parameterization in gated recurrent neural networks
- mikolov, 2014, Learning longer memory in recurrent neural networks
- 2016, A basic recurrent neural network model
- maas, 2011, Learning word vectors for sentiment analysis, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics Human Language Technologies, 1, 142
- chollet, 0, Keras Github
- 10.1162/089976600300015015 · DOI · Paper page
- zaremba, 2014, Recurrent Neural Network Regularization
- boulanger-lewandowski, 2012, Modeling Temporal Dependencies in High-Dimensional Sequences Application to Polyphonic Music Generation and Transcription
- 10.1109/72.279181 · DOI · Paper page
Cited by
Fink, O., Wang, Q., Svensén, M., Dersin, P., Lee, W.-J., & Ducoffe, M. (2020) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2020.103678 · Cited by 549
Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study
Canizo, M., Triguero, I., Conde, A., & Onieva, E. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.07.034 · Cited by 420
Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities
Ferrag, M. A., Alwahedi, F., Battah, A., Cherif, B., Mechri, A., Tihanyi, N., Bisztray, T., & Debbah, M. (2025) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2025.01.001 · Cited by 148
A novel monitoring system for fall detection in older people
Taramasco, C., Rodenas, T., Martinez, F., Fuentes, P., Munoz, R., Olivares, R., De Albuquerque, V. H. C., & Demongeot, J. (2018) · IEEE Access · doi:10.1109/access.2018.2861331 · Cited by 113
Ji, I. H., Lee, J. H., Kang, M. J., Park, W. J., Jeon, S. H., & Seo, J. T. (2024) · Sensors · doi:10.3390/s24030898 · Cited by 74
Metadata
- Authors
- Dey, R., & Salem, F. M.
- Venue
- 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)
- Publisher
- IEEE
- Published
- 2017-08
- Pages
- 1597-1600
- ISSN
- 1548-3746 (ISSN-L)
- OpenAlex
- W2580662672
- Cited by
- 2,186
- References
- 2
- FWCI
- 21.21
- Type
- conference paper
- Language
- en
- Open access
- green
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