Muhammad Alrabeiah

Electrical and Electronic Engineering · 2019

A researcher in the Science corpus: 2 papers; trusts 21 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Electrical and Electronic Engineering — 32 citations into it; #2,294 of 16,539 by trust here, #6,474 by citations
• Aerospace Engineering — 16 citations into it; #1,261 of 6,421 by trust here, #1,815 by citations
• Artificial Intelligence — 7 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Model Architectures — 5 citations into it
• Signal Processing — 5 citations into it

Whom Muhammad Alrabeiah trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Ahmed Alkhateeb
2. Simon S. Haykin
3. Robert W. Heath
4. Yann LeCun
5. Kurt Hornik
6. Yoshua Bengio
7. Anum Ali
8. Nuria González-Prelcic
9. Radford M. Neal
10. Matus Telgarsky

Who trusts Muhammad Alrabeiah overall

1. Tobias Lindstrøm Jensen
2. Beixiong Zheng
3. M. Di Renzo
4. Zhen-Qing He
5. Rui Zhang
6. Changsheng You
7. Ertugrul Basar
8. Qingqing Wu
9. Ying-Chang Liang
10. Yuanwei Liu

Papers from Muhammad Alrabeiah's perspective

Ordered by how strongly the researchers Muhammad Alrabeiah trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning (2019) — 737 citations
• Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6 GHz Channels (2019) — 349 citations