Haitham Bou-Ammar

Reinforcement Learning · 2010–2018

A researcher in the Science corpus: 11 papers; trusts 101 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 123 citations into it; #1,126 of 2,242 by trust here, #1,100 by citations
• Artificial Intelligence — 111 citations into it; #27,497 of 35,345 by trust here, #17,412 by citations
• Computational Theory and Mathematics — 21 citations into it
• Control and Systems Engineering — 20 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Management Science and Operations Research — 16 citations into it; #10,807 of 11,946 by trust here, #8,906 by citations
• Computer Vision and Pattern Recognition — 12 citations into it
• Meta & Continual Learning — 11 citations into it; #500 of 1,041 by trust here, #486 by citations

Whom Haitham Bou-Ammar trusts overall

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

1. Jan Peters
2. Matthew E. Taylor
3. Richard S. Sutton
4. Ronald J. Williams
5. S. Microtechnique
6. Geoffrey E. Hinton
7. Peter Stone
8. Stefan Schaal
9. Paul Ruvolo
10. Andrew Barto

Who trusts Haitham Bou-Ammar overall

1. Mohammad Rostami
2. Jorge Armando Mendez Mendez
3. Eric Eaton
4. Rasul Tutunov
5. David Isele
6. Sergio Valcarcel Macua
7. Zhiyuan Chen
8. Sanmit Narvekar
9. Daniel Jaymin Mankowitz
10. Joshua Achiam

Papers from Haitham Bou-Ammar's perspective

Ordered by how strongly the researchers Haitham Bou-Ammar 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. 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

• Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines (2013) — 26 citations
• Learning to Communicate Implicitly by Actions (2018) — 35 citations
• Scalable Multitask Policy Gradient Reinforcement Learning (2017) — 21 citations
• Controller design for quadrotor UAVs using reinforcement learning (2010) — 100 citations
• Model-Based Stabilisation of Deep Reinforcement Learning (2018)
• Distributed Multitask Reinforcement Learning with Quadratic Convergence (2018) — 9 citations
• Unsupervised Cross-Domain Transfer in Policy Gradient Reinforcement Learning via Manifold Alignment (2015) — 96 citations
• Autonomous Cross-Domain Knowledge Transfer in Lifelong Policy Gradient Reinforcement Learning (2015) — 81 citations
• Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret (2015) — 68 citations
• Online Multi-Task Learning for Policy Gradient Methods (2014) — 175 citations
• Reinforcement Learning Transfer Using a Sparse Coded Inter-task Mapping (2011) — 37 citations