Takayuki Osa

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 86 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it
• Reinforcement Learning — 79 citations into it
• Control and Systems Engineering — 62 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Robotics & Control — 23 citations into it
• Biomedical Engineering — 17 citations into it
• Cognitive Neuroscience — 16 citations into it
• Management Science and Operations Research — 13 citations into it

Whom Takayuki Osa trusts overall

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

1. Pieter Abbeel
2. Sergey Levine
3. Jan Peters
4. Richard S. Sutton
5. Geoffrey E. Hinton
6. Chelsea Finn
7. Radford M. Neal
8. Stefan Schaal
9. J. Bagnell
10. Andrew Y. Ng

Who trusts Takayuki Osa overall

1. Faraz Torabi
2. Yuping Luo
3. Muhammad Asif Rana
4. Rasool Fakoor
5. Corey Lynch
6. Daniel S. Brown
7. Garrett Warnell
8. Huazhe Xu
9. Feryal M. P. Behbahani
10. Daniel Seita

Papers from Takayuki Osa's perspective

Ordered by how strongly the researchers Takayuki Osa 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

• An Algorithmic Perspective on Imitation Learning (2018) — 1,031 citations
• Guiding Trajectory Optimization by Demonstrated Distributions (2017) — 63 citations
• Sample and Feedback Efficient Hierarchical Reinforcement Learning from Human Preferences (2018) — 23 citations