He Wen

Computer Vision and Pattern Recognition · with Zhou · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 175 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it; #5,589 of 21,575 by trust here, #2,625 by citations
• Artificial Intelligence — 43 citations into it; #9,665 of 35,345 by trust here, #4,370 by citations
• Efficiency & Compression — 25 citations into it; #368 of 1,264 by trust here, #207 by citations
• Model Architectures — 18 citations into it
• Media Technology — 12 citations into it; #1,733 of 3,621 by trust here, #729 by citations
• Optimization & Training — 10 citations into it

Whom He Wen trusts overall

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

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

Who trusts He Wen overall

1. Xue Yang
2. Minghui Liao
3. Zhen Dong
4. Yunhui Guo
5. Sungho Shin
6. Gabriele Prato
7. Tejalal Choudhary
8. By Lei Deng
9. Shangbang Long
10. Markus Nagel

Papers from He Wen's perspective

Ordered by how strongly the researchers He Wen 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients (2016) — 2,283 citations
• EAST: An Efficient and Accurate Scene Text Detector (2017) — 1,675 citations
• Effective Quantization Methods for Recurrent Neural Networks (2016) — 82 citations