Ahmed Hussain Qureshi

Reinforcement Learning · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 107 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 42 citations into it
• Artificial Intelligence — 40 citations into it; #14,304 of 35,345 by trust here, #30,347 by citations
• Computer Vision and Pattern Recognition — 28 citations into it
• Control and Systems Engineering — 19 citations into it
• Aerospace Engineering — 15 citations into it
• Robotics & Control — 11 citations into it
• Optimization & Training — 10 citations into it
• Cognitive Neuroscience — 5 citations into it; #3,345 of 10,852 by trust here, #9,453 by citations

Whom Ahmed Hussain Qureshi trusts overall

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

1. Sergey Levine
2. Pieter Abbeel
3. Steven M. LaValle
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Chelsea Finn
7. Alex Krizhevsky
8. Yoshua Bengio
9. John Schulman
10. Andrew Y. Ng

Who trusts Ahmed Hussain Qureshi overall

1. Faraz Torabi
2. Akanksha Saran
3. Sam Toyer
4. Liyiming Ke
5. Brian Hou
6. Garrett Warnell
7. Sanjiban Choudhury
8. Ruohan Zhang
9. Rohin Shah
10. Yinglong Miao

Papers from Ahmed Hussain Qureshi's perspective

Ordered by how strongly the researchers Ahmed Hussain Qureshi 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Motion Planning Networks (2018) — 294 citations
• Adversarial Imitation via Variational Inverse Reinforcement Learning (2018) — 65 citations
• Motion Planning Networks: Bridging the Gap Between Learning-Based and Classical Motion Planners (2019) — 318 citations