Lei Tai

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 10 papers; trusts 1,854 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 301 citations into it; #4,966 of 21,575 by trust here, #5,890 by citations
• Artificial Intelligence — 225 citations into it; #11,785 of 35,345 by trust here, #13,962 by citations
• Reinforcement Learning — 173 citations into it; #2,088 of 2,242 by trust here, #1,932 by citations
• Aerospace Engineering — 134 citations into it; #730 of 6,421 by trust here, #1,320 by citations
• Control and Systems Engineering — 83 citations into it
• Robotics & Control — 68 citations into it; #525 of 609 by trust here, #529 by citations
• Automotive Engineering — 36 citations into it
• Model Architectures — 29 citations into it

Whom Lei Tai trusts overall

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

1. David G. Lowe
2. Alex Krizhevsky
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Kaiming He
6. Sergey Levine
7. Ross B. Girshick
8. Yann LeCun
9. Volodymyr Mnih
10. Diederik P. Kingma

Who trusts Lei Tai overall

1. Jingwei Zhang
2. Sergey I. Nikolenko
3. Ming Liu
4. Joschka Boedecker
5. Shansi Zhang
6. Wolfram Burgard
7. Giuseppe Paolo
8. Farzad Niroui
9. Mark Pfeiffer
10. Kai Zhu

Papers from Lei Tai's perspective

Ordered by how strongly the researchers Lei Tai 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• VR-Goggles for Robots: Real-to-Sim Domain Adaptation for Visual Control (2018) — 135 citations
• Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation (2017) — 807 citations
• Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots (2016) — 114 citations
• Neural SLAM (2017) — 68 citations
• Socially Compliant Navigation Through Raw Depth Inputs with Generative Adversarial Imitation Learning (2017) — 206 citations
• MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships (2020) — 327 citations
• A robot exploration strategy based on Q-learning network (2016) — 152 citations
• A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation (2016) — 82 citations
• Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not (2016) — 78 citations
• A deep-network solution towards model-less obstacle avoidance (2016) — 215 citations