Xinwen Hou

Computer Vision and Pattern Recognition · 2001–2019

A researcher in the Science corpus: 3 papers; trusts 40 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 16 citations into it; #5,606 of 21,575 by trust here, #8,021 by citations
• Reinforcement Learning — 12 citations into it
• Artificial Intelligence — 7 citations into it; #18,181 of 35,345 by trust here, #34,213 by citations

Whom Xinwen Hou trusts overall

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

1. Hado van Hasselt
2. Richard S. Sutton
3. Volodymyr Mnih
4. Daniel D. Lee
5. David Silver
6. Simon M. M. Lucas
7. Navneet Dalal
8. Geoffrey E. Hinton
9. Fei Sha
10. Scott Fujimoto

Who trusts Xinwen Hou overall

1. Cong Yao
2. Yingying Zhu
3. Xiang Bai
4. Zheng Zhang
5. Karthik Devarajan
6. Qixiang Ye
7. Hailiang Xu
8. Che Wang
9. Jingu Kim
10. Chris H. Q. Ding

Papers from Xinwen Hou's perspective

Ordered by how strongly the researchers Xinwen Hou trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet Large Scale Visual Recognition Challenge
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Learning spatially localized, parts-based representation (2001) — 887 citations
• A Hybrid Approach to Detect and Localize Texts in Natural Scene Images (2011) — 441 citations
• Mixing Update Q-value for Deep Reinforcement Learning (2019) — 11 citations