Ahmed T. Elthakeb

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 25 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Efficiency & Compression — 9 citations into it
• Optimization & Training — 9 citations into it
• Safety, Robustness & Interpretability — 9 citations into it
• Model Architectures — 5 citations into it

Whom Ahmed T. Elthakeb trusts overall

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

1. Alex Krizhevsky
2. Barret Zoph
3. John Schulman
4. Geoffrey E. Hinton
5. Yihui He
6. Quoc V. Le
7. Yoshua Bengio
8. Diederik P. Kingma
9. Yann LeCun
10. Kaiming He

Who trusts Ahmed T. Elthakeb overall

1. Alexandre D'efossez
2. Qing Jin
3. Sein Park
4. Ruihao Gong
5. Yossi Adi
6. Linjie Yang
7. Junhyuk So
8. Gabriel Synnaeve

Papers from Ahmed T. Elthakeb's perspective

Ordered by how strongly the researchers Ahmed T. Elthakeb 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Going deeper with convolutions

Their papers

• ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks (2020) — 61 citations
• A Principled Approach to Learning Stochastic Representations for Privacy in Deep Neural Inference (2020) — 13 citations
• ReLeQ: An Automatic Reinforcement Learning Approach for Deep Quantization of Neural Networks (2018)