no code implementations • 9 Nov 2019 • Vicent Sanz Marco, Ben Taylor, Zheng Wang, Yehia Elkhatib
For image classification, we achieve a 1. 8x reduction in inference time with a 7. 52% improvement in accuracy, over the most-capable single DNN model.
no code implementations • 7 May 2019 • Faiza Samreen, Gordon S. Blair, Yehia Elkhatib
We overcome this through developing a Transfer Learning (TL) approach where the knowledge (in the form of the prediction model and associated data set) gained from running an application on a particular cloud infrastructure is transferred in order to substantially reduce the overhead of building new models for the performance of new applications and/or cloud infrastructures.
no code implementations • 5 Jul 2018 • Yehia Elkhatib
Data Science is currently a popular field of science attracting expertise from very diverse backgrounds.
no code implementations • 11 May 2018 • Ben Taylor, Vicent Sanz Marco, Willy Wolff, Yehia Elkhatib, Zheng Wang
This paper presents an adaptive scheme to determine which DNN model to use for a given input, by considering the desired accuracy and inference time.
no code implementations • 19 Sep 2017 • Muhammad Usama, Junaid Qadir, Aunn Raza, Hunain Arif, Kok-Lim Alvin Yau, Yehia Elkhatib, Amir Hussain, Ala Al-Fuqaha
We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking.
no code implementations • 5 Feb 2016 • Faiza Samreen, Yehia Elkhatib, Matthew Rowe, Gordon S. Blair
Decision making in cloud environments is quite challenging due to the diversity in service offerings and pricing models, especially considering that the cloud market is an incredibly fast moving one.