Ibrahim Sobh

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 157 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 162 citations into it
• Reinforcement Learning — 112 citations into it
• Computer Vision and Pattern Recognition — 100 citations into it
• Automotive Engineering — 61 citations into it
• Control and Systems Engineering — 38 citations into it
• Generative Models — 36 citations into it
• Robotics & Control — 27 citations into it
• Cognitive Neuroscience — 12 citations into it

Whom Ibrahim Sobh trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Andrew Barto
4. Pieter Abbeel
5. Diederik P. Kingma
6. Yoshua Bengio
7. Alex Krizhevsky
8. J. Urgen Schmidhuber
9. Andrew Y. Ng
10. Yann LeCun

Papers from Ibrahim Sobh's perspective

Ordered by how strongly the researchers Ibrahim Sobh 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. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Reinforcement Learning for Autonomous Driving: A Survey (2020) — 2,407 citations
• Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving (2019) — 60 citations
• Exploring applications of deep reinforcement learning for real-world autonomous driving systems (2019) — 81 citations