The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP
Abstract
The purpose of this article is to introduce the readers to the emerging technologies enabled by deep learning and to review the research work conducted in this area that is of direct relevance to signal processing. We also point out, in our view, the future research directions that may attract interests of and require efforts from more signal processing researchers and practitioners in this emerging area for advancing signal and information processing technology and applications.
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Cites
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
A unified architecture for natural language processing: deep neural networks with multitask learning
Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058
Cited by
A survey of deep neural network architectures and their applications
Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data
Philip Chen, C. L., & Zhang, C.-Y. (2014) · Information Sciences · doi:10.1016/j.ins.2014.01.015 · Cited by 3,047
Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746
Shi, J., Zheng, X., Li, Y., Zhang, Q., & Ying, S. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2655720 · Cited by 444
The state of the art of deep learning models in medical science and their challenges
Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K. U., & Kumar, A. (2021) · Multimedia Systems · doi:10.1007/s00530-020-00694-1 · Cited by 222
Shi, J., Zhou, S., Liu, X., Zhang, Q., Lu, M., & Wang, T. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.01.074 · Cited by 166
Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound
Wu, K., Chen, X., & Ding, M. (2014) · Optik · doi:10.1016/j.ijleo.2014.01.114 · Cited by 139
Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective
Zhang, S., Bamakan, S. M. H., Qu, Q., & Li, S. (2019) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2018.2864254 · Cited by 125
Soft errors in DNN accelerators: A comprehensive review
Ibrahim, Y., Wang, H., Liu, J., Wei, J., Chen, L., Rech, P., Adam, K., & Guo, G. (2020) · Microelectronics Reliability · doi:10.1016/j.microrel.2020.113969 · Cited by 102
Caroppo, A., Leone, A., & Siciliano, P. (2020) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-9665-4 · Cited by 60
Metadata
- Authors
- Yu, D., & Deng, L.
- Venue
- IEEE Signal Processing Magazine
- Published
- 2011-01
- Volume
- 28
- Issue
- 1
- Pages
- 145-154
- ISSN
- 1053-5888 (ISSN-L)
- OpenAlex
- W2093866254
- Cited by
- 455
- References
- 2
- FWCI
- 3.40
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
