Ardavan Saeedi

Reinforcement Learning · 2016

A researcher in the Science corpus: 2 papers; trusts 36 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 50 citations into it; #514 of 2,242 by trust here, #436 by citations
• Artificial Intelligence — 47 citations into it; #4,814 of 35,345 by trust here, #4,602 by citations
• Computer Vision and Pattern Recognition — 22 citations into it
• Cognitive Neuroscience — 15 citations into it; #6,515 of 10,852 by trust here, #5,162 by citations
• Computational Theory and Mathematics — 10 citations into it
• Management Science and Operations Research — 7 citations into it; #7,638 of 11,946 by trust here, #3,825 by citations
• Representation & Self-Supervised Learning — 7 citations into it
• Cognitive Science & Psychology — 5 citations into it

Whom Ardavan Saeedi trusts overall

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

1. Andrew Barto
2. Richard S. Sutton
3. Peter S. Dayan
4. Volodymyr Mnih
5. Geoffrey E. Hinton
6. Tom Schaul
7. J. Urgen Schmidhuber
8. Alex Graves
9. Satinder Singh
10. Shakir Mohamed

Who trusts Ardavan Saeedi overall

1. Alexey Dosovitskiy
2. Marlos C. Machado
3. Sergey Levine
4. Niels Justesen
5. Fei Xia
6. Vladlen Koltun
7. Michael Crawshaw
8. Abhinav Kumar Gupta
9. Matteo Hessel
10. Martin A. Riedmiller

Papers from Ardavan Saeedi's perspective

Ordered by how strongly the researchers Ardavan Saeedi 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. Et al
10. Human-level control through deep reinforcement learning

Their papers

• Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation (2016) — 1,297 citations
• Deep Successor Reinforcement Learning (2016) — 232 citations