Yongming Shen

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 85 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 39 citations into it; #13,495 of 21,575 by trust here, #12,419 by citations
• Electrical and Electronic Engineering — 27 citations into it; #7,632 of 16,539 by trust here, #8,802 by citations
• Artificial Intelligence — 18 citations into it; #21,785 of 35,345 by trust here, #18,586 by citations
• Efficiency & Compression — 9 citations into it; #764 of 1,264 by trust here, #925 by citations
• Model Architectures — 7 citations into it

Whom Yongming Shen trusts overall

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

1. Alex Krizhevsky
2. Karen Simonyan
3. Geoffrey E. Hinton
4. Clément Farabet
5. Ilya Sutskever
6. Andrew Zisserman
7. Song Han
8. Yann LeCun
9. Christian Szegedy
10. Srimat T. Chakradhar

Who trusts Yongming Shen overall

1. Sparsh Mittal
2. Ahmad Shawahna
3. Md Maruf Hossain Shuvo
4. Duy-Thanh Nguyen
5. Weiwen Jiang
6. Joel Hestness
7. Sadiq M. Sait
8. Syed Kamrul Islam
9. Tuan Nghia Nguyen
10. Nitin Rathi

Papers from Yongming Shen's perspective

Ordered by how strongly the researchers Yongming Shen 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Going deeper with convolutions
7. ImageNet: A large-scale hierarchical image database
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Maximizing CNN accelerator efficiency through resource partitioning (2016) — 336 citations
• Escher: A CNN Accelerator with Flexible Buffering to Minimize Off-Chip Transfer (2017) — 106 citations