Sherif Sakr

Artificial Intelligence · 2011–2019

A researcher in the Science corpus: 5 papers; trusts 119 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #15,357 of 35,345 by trust here, #28,481 by citations
• Information Systems — 37 citations into it; #7,466 of 8,771 by trust here, #5,303 by citations
• Computer Networks and Communications — 33 citations into it; #8,687 of 9,886 by trust here, #6,217 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #19,242 of 21,575 by trust here, #16,367 by citations
• Neural Architecture Search & AutoML — 28 citations into it; #137 of 283 by trust here, #266 by citations
• Optimization & Training — 26 citations into it
• Management Science and Operations Research — 15 citations into it
• Model Architectures — 11 citations into it

Whom Sherif Sakr trusts overall

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

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

Who trusts Sherif Sakr overall

1. Philip Chen
2. Radwa El Shawi
3. Katarina Grolinger
4. Chunming Zhang
5. Marc-André Zöller
6. Mohamed Maher
7. Li Yang
8. Daniel Mesafint Belete
9. Wilson Akio Higashino

Papers from Sherif Sakr's perspective

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

Their papers

• Automated Machine Learning: State-of-The-Art and Open Challenges (2019) — 205 citations
• SmartML: A Meta Learning-Based Framework for Automated Selection and Hyperparameter Tuning for Machine Learning Algorithms (2019) — 50 citations
• Encyclopedia of Big Data Technologies (2019) — 58 citations
• Handbook of Big Data Technologies (2017) — 76 citations
• A Survey of Large Scale Data Management Approaches in Cloud Environments (2011) — 472 citations