Pinxin Long

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 109 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 59 citations into it; #11,787 of 21,575 by trust here, #13,269 by citations
• Artificial Intelligence — 50 citations into it; #18,763 of 35,345 by trust here, #21,878 by citations
• Aerospace Engineering — 36 citations into it
• Reinforcement Learning — 29 citations into it
• Control and Systems Engineering — 15 citations into it; #5,192 of 8,257 by trust here, #6,083 by citations
• Computer Networks and Communications — 13 citations into it
• Robotics & Control — 13 citations into it
• Automotive Engineering — 10 citations into it; #2,195 of 3,081 by trust here, #2,332 by citations

Whom Pinxin Long trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. John Schulman
4. Jur van den Berg
5. Yoshua Bengio
6. Sergey Levine
7. Alex Krizhevsky
8. Kaiming He
9. Alex Graves
10. Yann LeCun

Who trusts Pinxin Long overall

1. Tingxiang Fan
2. Changan Chen
3. Kai Zhu
4. Yang Lyu
5. Lei Tai
6. Boyi Liu
7. Wenxi Liu
8. Yuejiang Liu
9. Jia Pan
10. Angela Dai

Papers from Pinxin Long's perspective

Ordered by how strongly the researchers Pinxin Long 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Data-driven contextual modeling for 3D scene understanding (2016) — 58 citations
• Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios (2020) — 351 citations
• Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning (2017) — 530 citations
• Deep-Learned Collision Avoidance Policy for Distributed Multiagent Navigation (2016) — 191 citations