Anis Elgabli

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 8 papers; trusts 69 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 190 citations into it; #22,929 of 35,345 by trust here, #17,412 by citations
• Computer Networks and Communications — 107 citations into it; #4,203 of 9,886 by trust here, #3,426 by citations
• Optimization & Training — 71 citations into it; #2,293 of 2,601 by trust here, #2,162 by citations
• Electrical and Electronic Engineering — 68 citations into it; #3,430 of 16,539 by trust here, #9,210 by citations
• Federated & Distributed Learning — 58 citations into it
• Computational Mechanics — 57 citations into it; #6,302 of 10,001 by trust here, #5,567 by citations
• Efficiency & Compression — 33 citations into it
• ML Systems & Hardware — 33 citations into it; #429 of 575 by trust here, #493 by citations

Whom Anis Elgabli trusts overall

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

1. Angelia Nedić
2. Jihong Park
3. Richard S. Sutton
4. Mohammad Mohammadi Amiri
5. H. Brendan McMahan
6. Wei Deng
7. Amrit Singh Bedi
8. Stephen P. Boyd
9. Medhi Bennis
10. Alexander Olshevsky

Who trusts Anis Elgabli overall

1. Jihong Park
2. Chaouki Ben Issaid
3. Sumudu Samarakoon
4. Khaled Ben Letaief
5. Medhi Bennis
6. Yuanming Shi
7. Merouane Debbah
8. Jianmin Lu
9. Joongheon Kim
10. Jianhua Lu

Papers from Anis Elgabli's perspective

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

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

Their papers

• GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning (2019) — 91 citations
• Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning (2019) — 74 citations
• Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning (2020) — 72 citations
• Reinforcement Learning Based Scheduling Algorithm for Optimizing Age of Information in Ultra Reliable Low Latency Networks (2018) — 46 citations
• Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020) — 185 citations
• Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM (2020) — 13 citations
• Extreme URLLC: Vision, Challenges, and Key Enablers (2020) — 109 citations
• Distilling On-Device Intelligence at the Network Edge (2019) — 33 citations