Yi Sun

Computer Vision and Pattern Recognition · with Tang · 2013–2015

A researcher in the Science corpus: 7 papers; trusts 917 researchers and is trusted by 2,813 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 153 citations into it; #710 of 21,575 by trust here, #1,074 by citations
• Signal Processing — 45 citations into it; #115 of 7,572 by trust here, #128 by citations
• Artificial Intelligence — 38 citations into it; #15,545 of 35,345 by trust here, #25,040 by citations
• Model Architectures — 15 citations into it; #462 of 2,278 by trust here, #740 by citations
• Representation & Self-Supervised Learning — 13 citations into it; #350 of 3,145 by trust here, #483 by citations
• Evaluation & Benchmarks — 9 citations into it
• Efficiency & Compression — 8 citations into it
• Optimization & Training — 7 citations into it

Whom Yi Sun trusts overall

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

1. Yann LeCun
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Dong Chen
5. Karen Simonyan
6. Xudong Cao
7. Yoshua Bengio
8. Gary B. Huang
9. Ilya Sutskever
10. Xiaogang Wang

Who trusts Yi Sun overall

1. Ping Luo
2. Xiaogang Wang
3. Xiaoou Tang
4. Yandong Wen
5. Weiyang Liu
6. Zhenyao Zhu
7. Xiang Wu
8. Chen Change Loy
9. Changxing Ding
10. Wanli Ouyang

Papers from Yi Sun's perspective

Ordered by how strongly the researchers Yi Sun 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep Learning Face Representation by Joint Identification-Verification (2014) — 2,318 citations
• Deep Convolutional Network Cascade for Facial Point Detection (2013) — 1,418 citations
• Deep Learning Face Representation from Predicting 10,000 Classes (2014) — 2,013 citations
• Deeply learned face representations are sparse, selective, and robust (2014) — 955 citations
• DeepID3: Face Recognition with Very Deep Neural Networks (2015) — 975 citations
• Hybrid Deep Learning for Face Verification (2013) — 401 citations
• Sparsifying Neural Network Connections for Face Recognition (2015) — 145 citations