Ting Zhang

Computer Vision and Pattern Recognition · with Wang · 2015–2018

A researcher in the Science corpus: 5 papers; trusts 861 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 191 citations into it; #16,269 of 21,575 by trust here, #7,852 by citations
• Artificial Intelligence — 99 citations into it; #34,501 of 35,345 by trust here, #19,873 by citations
• Model Architectures — 50 citations into it
• Information Retrieval & Search — 40 citations into it
• Efficiency & Compression — 39 citations into it
• Optimization & Training — 21 citations into it
• Aerospace Engineering — 18 citations into it
• Media Technology — 17 citations into it

Whom Ting Zhang trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. David G. Lowe
5. Antonio Torralba
6. Yoshua Bengio
7. Karen Simonyan
8. Ilya Sutskever
9. Yann LeCun
10. Ross B. Girshick

Who trusts Ting Zhang overall

1. Jingdong Wang
2. Gao Huang
3. Yi Wei
4. Xiaojie Jin
5. Guotian Xie
6. Xiang Wu
7. Liang Zheng
8. Ruiqi Guo
9. Matthijs Douze
10. Mingsheng Long

Papers from Ting Zhang's perspective

Ordered by how strongly the researchers Ting Zhang 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Survey on Learning to Hash (2016) — 1,084 citations
• Interleaved Group Convolutions for Deep Neural Networks (2017) — 88 citations
• Interleaved Structured Sparse Convolutional Neural Networks (2018) — 110 citations
• Sparse composite quantization (2015) — 80 citations
• Deeply-Fused Nets (2016) — 103 citations