Learning deep hierarchical and temporal recurrent neural networks with residual learning-Reference-Cited by-同舟云学术

Learning deep hierarchical and temporal recurrent neural networks with residual learning

Published:2020-01-29 Issue:4 Volume:11 Page:873-882
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Zia Tehseen^ORCID,Abbas Assad,Habib Usman,Khan Muhammad Sajid

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s13042-020-01063-0.pdf

Reference40 articles.

1. Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127

2. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

3. Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning. https://arxiv.org/abs/arXiv:1506.00019

4. Mikolov T, Karafiát M, Burget L, Cernocký J, Khudanpur S (2010) Recurrent neural network based language model. Interspeech 2:3

5. Graves A, Mohamed AR, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 6645–6649

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Vehicle speed prediction using a convolutional neural network combined with a gated recurrent unit with attention;Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering;2024-02-03

2. Deep adversarial reconstruction classification network for unsupervised domain adaptation;International Journal of Machine Learning and Cybernetics;2023-12-19

3. Workplace Safety Training for Healthcare Workers Based on Deep Learning;2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS);2023-11-16

4. Multi-view feature fusion fault diagnosis method based on an improved temporal convolutional network;Insight - Non-Destructive Testing and Condition Monitoring;2023-10-01

5. Fast Recognition of Snap-Fit for Industrial Robot Using a Recurrent Neural Network;IEEE Robotics and Automation Letters;2023-03