Mohammed Abdou

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 28 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it; #13,236 of 35,345 by trust here, #12,974 by citations
• Reinforcement Learning — 22 citations into it; #1,485 of 2,242 by trust here, #1,344 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Cognitive Neuroscience — 6 citations into it
• Model Architectures — 5 citations into it

Whom Mohammed Abdou 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. Christopher J. C. H. Watkins
8. Geoffrey E. Hinton
9. Matthew J. Hausknecht
10. J. Urgen Schmidhuber

Who trusts Mohammed Abdou 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 Mohammed Abdou's perspective

Ordered by how strongly the researchers Mohammed Abdou trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Long Short-Term Memory
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep Reinforcement Learning framework for Autonomous Driving (2017) — 1,062 citations
• End-to-End Deep Reinforcement Learning for Lane Keeping Assist (2016) — 184 citations