Radwa El Shawi

Artificial Intelligence · 2019

A researcher in the Science corpus: 1 paper; trusts 115 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #17,776 of 35,345 by trust here, #29,412 by citations
• Computer Vision and Pattern Recognition — 30 citations into it
• Neural Architecture Search & AutoML — 26 citations into it; #138 of 283 by trust here, #266 by citations
• Optimization & Training — 22 citations into it
• Management Science and Operations Research — 13 citations into it
• Information Systems — 12 citations into it
• Model Architectures — 11 citations into it
• Computational Theory and Mathematics — 8 citations into it

Whom Radwa El Shawi trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Alex Krizhevsky
5. James Bergstra
6. Ronald J. Williams
7. Barret Zoph
8. Matthew D. Zeiler
9. Frank Hutter
10. Karen Simonyan

Who trusts Radwa El Shawi overall

1. Marc-André Zöller
2. Li Yang
3. Daniel Mesafint Belete
4. Marco F. Huber
5. Yufan Jiang
6. Abdallah Shami
7. Manjaiah Doddaghatta Huchaiah

Papers from Radwa El Shawi's perspective

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

Their papers

• Automated Machine Learning: State-of-The-Art and Open Challenges (2019) — 205 citations