Xin Yu Tan

Reinforcement Learning · 2015

A researcher in the Science corpus: 2 papers; trusts 49 researchers and is trusted by 176 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 50 citations into it; #1,782 of 2,242 by trust here, #1,663 by citations
• Artificial Intelligence — 41 citations into it; #8,321 of 35,345 by trust here, #7,413 by citations
• Computer Vision and Pattern Recognition — 18 citations into it; #2,755 of 21,575 by trust here, #4,623 by citations
• Control and Systems Engineering — 16 citations into it; #1,649 of 8,257 by trust here, #521 by citations
• Robotics & Control — 13 citations into it; #151 of 609 by trust here, #139 by citations
• Biomedical Engineering — 12 citations into it
• Management Science and Operations Research — 8 citations into it
• Aerospace Engineering — 6 citations into it

Whom Xin Yu Tan trusts overall

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

1. Sergey Levine
2. Geoffrey E. Hinton
3. Marc Peter Deisenroth
4. Jan Peters
5. Christian Szegedy
6. Leemon C. Baird III
7. Sergey Ioffe
8. Alex Krizhevsky
9. Yann LeCun
10. Pieter Abbeel

Who trusts Xin Yu Tan overall

1. Sergey Levine
2. Chelsea Finn
3. Pieter Abbeel
4. Pierre Sermanet
5. Frederik D. Ebert
6. Lei Tai
7. Timothée Lesort
8. Herke Van Hoof
9. David R Ha
10. Jitendra Malik

Papers from Xin Yu Tan's perspective

Ordered by how strongly the researchers Xin Yu Tan 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep spatial autoencoders for visuomotor learning (2015) — 598 citations
• Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders (2015) — 81 citations