Guorong Wu

Computer Vision and Pattern Recognition · with Shen · 2015–2017

A researcher in the Science corpus: 2 papers; trusts 176 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 70 citations into it; #10,839 of 21,575 by trust here, #4,955 by citations
• Artificial Intelligence — 41 citations into it; #28,865 of 35,345 by trust here, #8,007 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Radiology, Nuclear Medicine and Imaging — 17 citations into it; #2,243 of 3,265 by trust here, #597 by citations
• Model Architectures — 14 citations into it
• Optimization & Training — 11 citations into it
• Biomedical Engineering — 6 citations into it
• Media Technology — 6 citations into it

Whom Guorong Wu trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Honglak Lee
4. Alex Krizhevsky
5. Yann LeCun
6. Hugo Larochelle
7. Ilya Sutskever
8. J. Urgen Schmidhuber
9. Ruslan Salakhutdinov
10. David G. Lowe

Who trusts Guorong Wu overall

1. Arvid Lundervold
2. V. Kearney
3. Dinggang Shen
4. M. Bakator
5. Rui Liao
6. Berkman Sahiner
7. Sam Haaf
8. Mufti Mahmud
9. Ahmed Hosny
10. Tao Lei

Papers from Guorong Wu's perspective

Ordered by how strongly the researchers Guorong Wu 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Learning in Medical Image Analysis (2017) — 2,935 citations
• Scalable High Performance Image Registration Framework by Unsupervised Deep Feature Representations Learning (2015) — 274 citations