Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Rezk, Nesma M."'
Autor:
Rezk, Nesma M., Nordström, Tomas, Stathis, Dimitrios, Ul-Abdin, Zain, Aksoy, Eren Erdal, Hemani, Ahmed
The compression of deep learning models is of fundamental importance in deploying such models to edge devices. The selection of compression parameters can be automated to meet changes in the hardware platform and application using optimization algori
Externí odkaz:
http://arxiv.org/abs/2108.01192
Recurrent Neural Networks (RNNs) are a class of machine learning algorithms used for applications with time-series and sequential data. Recently, there has been a strong interest in executing RNNs on embedded devices. However, difficulties have arise
Externí odkaz:
http://arxiv.org/abs/1908.07062
Autor:
Rezk, Nesma M., Nordström, Tomas, Stathis, Dimitrios, Ul-Abdin, Zain, Aksoy, Eren Erdal, Hemani, Ahmed
Publikováno v:
In Journal of Systems Architecture December 2022 133
Autor:
Rezk, Nesma M.1 (AUTHOR) nesma.rezk@hh.se, Nordström, Tomas2 (AUTHOR) tomas.nordstrom@umu.se, Ul-Abdin, Zain1 (AUTHOR)
Publikováno v:
Information (2078-2489). Apr2022, Vol. 13 Issue 4, p176-176. 16p.
Akademický článek
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Publikováno v:
Progress in Artificial Intelligence: 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Coimbra, Portugal, September 8-11, 2015. Proceedings; 2015, p493-498, 6p
Publikováno v:
2014 9th International Conference on Computer Engineering & Systems (ICCES); 2014, p170-174, 5p
Publikováno v:
Progress in Artificial Intelligence: 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Coimbra, Portugal, September 8-11, 2015. Proceedings; 2015, p809-811, 3p
This book constitutes the refereed proceedings of the 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, held in Coimbra, Portugal, in September 2015. The 45 revised full papers presented together with 36 revised short papers were care