2 citations · 2 across the 5 of their papers we have counts for
5 papers
FAIR-Pruner: A Flexible Framework for Automatic Layer-Wise Pruning via Tolerance of Difference
Chenqing Lin, Mostafa Hussien, Chengyao Yu +4
Structured pruning is a standard tool for compressing deep neural networks, but its practical performance depends on how sparsity is allocated across layers. We propose FAIR-Pruner…
Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance
Mostafa Hussien, Mahmoud Afifi, Kim Khoa Nguyen +1
Recent advancements have scaled neural networks to unprecedented sizes, achieving remarkable performance across a wide range of tasks. However, deploying these large-scale models o…
Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey
Mostafa Hussien, Islam A. T. F. Taj-Eddin, Mohammed F. A. Ahmed +3
The last decade, (2012 - 2022), saw an unprecedented advance in machine learning (ML) techniques, particularly deep learning (DL). As a result of the proven capabilities of DL, a l…
A Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoT
Mostafa Hussien, Kim Khoa Nguyen, Mohamed Cheriet
In wireless Internet of things (IoT), the sensors usually have limited bandwidth and power resources. Therefore, in a distributed setup, each sensor should compress and quantize th…
PRVNet: A Novel Partially-Regularized Variational Autoencoders for Massive MIMO CSI Feedback
Mostafa Hussien, Kim Khoa Nguyen, Mohamed Cheriet
In a multiple-input multiple-output frequency-division duplexing (MIMO-FDD) system, the user equipment (UE) sends the downlink channel state information (CSI) to the base station t…