Shuang Wu

Computer Vision and Pattern Recognition · with Wu · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 108 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 60 citations into it; #20,353 of 21,575 by trust here, #8,612 by citations
• Artificial Intelligence — 52 citations into it; #33,223 of 35,345 by trust here, #12,387 by citations
• Efficiency & Compression — 16 citations into it; #1,177 of 1,264 by trust here, #685 by citations
• Model Architectures — 15 citations into it
• Optimization & Training — 14 citations into it

Whom Shuang Wu trusts overall

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

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

Who trusts Shuang Wu overall

1. By Lei Deng
2. Alexandre D'efossez
3. Ying Wang
4. Rudra P. K. Poudel
5. Ron Banner
6. Guoqi Li
7. Naveen Mellempudi
8. Tailin Liang
9. Ahmed Imteaj
10. Oded Padon

Papers from Shuang Wu's perspective

Ordered by how strongly the researchers Shuang 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Training and Inference with Integers in Deep Neural Networks (2018) — 419 citations
• $L1$ -Norm Batch Normalization for Efficient Training of Deep Neural Networks (2018) — 128 citations
• Towards artificial general intelligence with hybrid Tianjic chip architecture (2019) — 1,085 citations