Yu Fan Chen

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 21 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 30 citations into it; #8,858 of 21,575 by trust here, #11,721 by citations
• Artificial Intelligence — 18 citations into it; #8,278 of 35,345 by trust here, #12,761 by citations
• Aerospace Engineering — 11 citations into it
• Reinforcement Learning — 10 citations into it
• Automotive Engineering — 7 citations into it; #197 of 3,081 by trust here, #1,117 by citations
• Computer Networks and Communications — 7 citations into it
• Control and Systems Engineering — 6 citations into it

Whom Yu Fan Chen trusts overall

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

1. Jur van den Berg
2. Pieter Abbeel
3. Diederik P. Kingma
4. Volodymyr Mnih
5. Ilya Sutskever
6. Geoffrey E. Hinton
7. Ming C Lin
8. Andrew Y. Ng
9. Stephen J. Guy
10. Henrik Kretzschmar

Who trusts Yu Fan Chen overall

1. Tingxiang Fan
2. Pinxin Long
3. Michael Everett
4. Kai Zhu
5. Lei Tai
6. Christoforos Mavrogiannis
7. Changan Chen
8. Tao Zhang
9. Wenxi Liu
10. Jingwei Zhang

Papers from Yu Fan Chen's perspective

Ordered by how strongly the researchers Yu Fan Chen 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning (2016) — 697 citations
• Socially aware motion planning with deep reinforcement learning (2017) — 777 citations
• Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning (2018) — 618 citations
• Duckietown: An open, inexpensive and flexible platform for autonomy education and research (2017) — 247 citations