Prof. Fathi M. Salem

Michigan State University, USA

Email: [email protected]

Qualifications

1983 Ph.D., Department of Electrical Engineering, Computer Sciences, University of California, USA

Publications (Selected)

1. Salem, F. M. (2022). Video deep learning classification for autonomous vehicle navigation. 2022 IEEE International Symposium on Circuits and Systems (ISCAS), 1014–1017.
2. Salem, F. M. (2022). Recurrent neural networks. Springer International Publishing.
3. Albataineh, Z., & Salem, F. (2021). Two pairwise iterative schemes for high dimensional blind source separation. International Journal of Speech Technology, 24(4), 957–968.
4. Salem, F. M. (2021). Learning processes. In Recurrent neural networks: From simple to gated architectures (pp. 21–39).
5. Salem, F. M. (2021). Network architectures. In Recurrent neural networks: From simple to gated architectures (pp. 3–19).
6. Salem, F. M. (2021). Gated RNN: The minimal gated unit (MGU) RNN. In Recurrent neural networks: From simple to gated architectures (pp. 101–113).
7. Salem, F. M. (2021). Gated RNN: The long short-term memory (LSTM) RNN. In Recurrent neural networks: From simple to gated architectures (pp. 71–82).
8. Salem, F. M. (2021). Gated RNN: The gated recurrent unit (GRU) RNN. In Recurrent neural networks: From simple to gated architectures (pp. 85–100).
9. Albataineh, Z., & Salem, F. M. (2021). A RobustICA-based algorithmic system for blind separation of convolutive mixtures. International Journal of Speech Technology, 24(3), 701–713.
10. Salem, F. M. (2021). Unbounded capacity associative memory for real-valued pattern storage and recall. 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS).
11. Gopalakrishnan, K., & Salem, F. M. (2020). Sentiment analysis using simplified long short-term memory recurrent neural networks. arXiv preprint arXiv:2005.03993.
12. Salem, F. M. (2019). Slim LSTMs: Parameter-reductions within gating signals. 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS).
13. Akandeh, A., & Salem, F. M. (2019). Slim LSTM networks: Lstm_6 and lstm_c6. 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS).
14. Kent, D., & Salem, F. (2019). Performance of three slim variants of the long short-term memory (LSTM) layer. 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS).
15. Salem, F. M. (2019). Systems, methods, and media for gated recurrent neural networks with reduced parameter gating signals and/or memory-cell units (U.S. Patent Application No. 16/178,029). U.S. Patent and Trademark Office.
16. Salem, F. M. (2018). Slim LSTMs. arXiv preprint arXiv:1812.11391.
17. Abusaid, O. M., & Salem, F. M. (2017). Kullback-Leibler divergence minimization for competitive learning of self-organizing maps. 2017 International Conference on Engineering and Technology (ICET), 1–6.
18. Abusaid, O. M., & Salem, F. M. (2017). Self-organizing techniques to resource block scheduling in LTE-A communication systems. 2017 International Conference on Engineering and Technology (ICET), 1–6.
19. Dey, R., & Salem, F. M. (2017). Gate-variants of gated recurrent unit (GRU) neural networks. 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).
20. Lu, Y., & Salem, F. M. (2017). Simplified gating in long short-term memory (LSTM) recurrent neural networks. 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).
21. Heck, J. C., & Salem, F. M. (2017). Simplified minimal gated unit variations for recurrent neural networks. 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS).
22. Albataineh, Z., & Salem, F. M. (2017). Adaptive blind CDMA receivers based on ICA filtered structures. Circuits, Systems, and Signal Processing, 36(8), 3320–3348.

Profile Details
https://www.egr.msu.edu/~salem/
https://scholar.google.com/citations?user=Ou60q4gAAAAJ&hl=en
https://www.researchgate.net/profile/Fathi-Salem

WOS ResearcherID： FVN-7754-2022