Wenxi Liu

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 4 papers; trusts 261 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 82 citations into it; #11,788 of 21,575 by trust here, #10,500 by citations
• Artificial Intelligence — 58 citations into it; #18,762 of 35,345 by trust here, #21,878 by citations
• Reinforcement Learning — 29 citations into it
• Aerospace Engineering — 24 citations into it
• Control and Systems Engineering — 14 citations into it; #5,191 of 8,257 by trust here, #6,083 by citations
• Computer Networks and Communications — 13 citations into it
• Cognitive Neuroscience — 12 citations into it
• Optimization & Training — 12 citations into it

Whom Wenxi Liu trusts overall

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

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

Who trusts Wenxi Liu overall

1. Tingxiang Fan
2. Pinxin Long
3. Kai Zhu
4. Yang Lyu
5. Changan Chen
6. Lei Tai

Papers from Wenxi Liu's perspective

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

Their papers

• SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection (2015) — 297 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