Duo Wang

Computer Vision and Pattern Recognition · with Cheng · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 515 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 126 citations into it; #13,701 of 21,575 by trust here, #5,507 by citations
• Artificial Intelligence — 108 citations into it
• Efficiency & Compression — 82 citations into it; #779 of 1,264 by trust here, #899 by citations
• Model Architectures — 42 citations into it
• Optimization & Training — 23 citations into it
• Signal Processing — 11 citations into it
• Computational Mechanics — 8 citations into it
• Neurology — 6 citations into it

Whom Duo Wang trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Yann LeCun
5. Ilya Sutskever
6. Kaiming He
7. Karen Simonyan
8. Diederik P. Kingma
9. Christian Szegedy
10. Andrew Zisserman

Who trusts Duo Wang overall

1. Kai Yang
2. Yuanming Shi
3. Jing Zhang
4. Thomas Elsken
5. Fahad Sarfraz
6. Weiyu Cheng
7. Md Maruf Hossain Shuvo
8. Kai Y. Xiao
9. Jaemin Yoo
10. Boris Knyazev

Papers from Duo Wang's perspective

Ordered by how strongly the researchers Duo Wang 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• A Survey of Model Compression and Acceleration for Deep Neural Networks (2017) — 1,247 citations
• Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges (2018) — 686 citations