Yunfei Bai

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 7 papers; trusts 285 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 121 citations into it
• Computer Vision and Pattern Recognition — 105 citations into it; #15,963 of 21,575 by trust here, #4,137 by citations
• Control and Systems Engineering — 73 citations into it; #5,773 of 8,257 by trust here, #269 by citations
• Reinforcement Learning — 72 citations into it; #2,018 of 2,242 by trust here, #965 by citations
• Biomedical Engineering — 42 citations into it; #8,879 of 10,734 by trust here, #3,600 by citations
• Robotics & Control — 27 citations into it; #400 of 609 by trust here, #107 by citations
• Representation & Self-Supervised Learning — 21 citations into it
• Aerospace Engineering — 20 citations into it

Whom Yunfei Bai trusts overall

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

1. Sergey Levine
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Ross B. Girshick
7. Yoshua Bengio
8. Yann LeCun
9. Jimmy Ba
10. Ilya Sutskever

Who trusts Yunfei Bai overall

1. OpenAI
2. Priyanka Mandikal
3. Tao Chen
4. Sammy Joe Christen
5. Herke Van Hoof
6. Kristen Grauman
7. Abhishek Gupta
8. Jie Xu
9. Stefan Stevšić
10. Sergey Levine

Papers from Yunfei Bai's perspective

Ordered by how strongly the researchers Yunfei Bai 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping (2018) — 731 citations
• Sim-to-Real: Learning Agile Locomotion For Quadruped Robots (2018) — 981 citations
• RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer (2020) — 132 citations
• Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation (2017) — 129 citations
• Watch, Try, Learn: Meta-Learning from Demonstrations and Reward (2019) — 54 citations
• Learning Grasping Interaction with Geometry-aware 3D Representations (2017) — 8 citations
• Dexterous manipulation using both palm and fingers (2014) — 138 citations