Ahmad El Sallab

Computer Vision and Pattern Recognition · with Yogamani · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 276 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 80 citations into it
• Artificial Intelligence — 33 citations into it; #13,237 of 35,345 by trust here, #12,974 by citations
• Reinforcement Learning — 23 citations into it; #1,486 of 2,242 by trust here, #1,344 by citations
• Aerospace Engineering — 19 citations into it
• Environmental Engineering — 10 citations into it
• Evaluation & Benchmarks — 9 citations into it
• 3D Vision & Pose — 6 citations into it
• Cognitive Neuroscience — 6 citations into it

Whom Ahmad El Sallab trusts overall

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

1. Richard S. Sutton
2. Ronald J. Williams
3. Volodymyr Mnih
4. Ben J. A. Kröse
5. Sepp Hochreiter
6. Pieter Abbeel
7. Geoffrey E. Hinton
8. J. Urgen Schmidhuber
9. Christopher J. C. H. Watkins
10. Matthew J. Hausknecht

Who trusts Ahmad El Sallab overall

1. Sergey I. Nikolenko
2. Edouard Leurent
3. Praveen Palanisamy
4. Shansi Zhang
5. Krzysztof Czarnecki
6. Pin Wang
7. Ammar Haydari
8. Jianyu Chen
9. Shuyang Du
10. Victor Talpaert

Papers from Ahmad El Sallab's perspective

Ordered by how strongly the researchers Ahmad El Sallab 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. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Deep Reinforcement Learning framework for Autonomous Driving (2017) — 1,062 citations
• YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud (2018) — 172 citations
• RGB and LiDAR fusion based 3D Semantic Segmentation for Autonomous Driving (2019) — 128 citations
• MODNet: Motion and Appearance based Moving Object Detection Network for Autonomous Driving (2018) — 92 citations
• End-to-End Deep Reinforcement Learning for Lane Keeping Assist (2016) — 184 citations