Jing Pu

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 59 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 37 citations into it; #13,831 of 21,575 by trust here, #3,393 by citations
• Electrical and Electronic Engineering — 19 citations into it; #8,031 of 16,539 by trust here, #2,583 by citations
• Efficiency & Compression — 16 citations into it; #972 of 1,264 by trust here, #259 by citations
• Model Architectures — 11 citations into it

Whom Jing Pu trusts overall

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

1. Karen Simonyan
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Ross B. Girshick
6. Andrew Zisserman
7. Yu-hsin Chen
8. Yann LeCun
9. Song Han
10. X. Zhang

Who trusts Jing Pu overall

1. Song Han
2. Vivienne Sze
3. Linghao Song
4. Yu-hsin Chen
5. Haichuan Yang
6. Huizi Mao

Papers from Jing Pu's perspective

Ordered by how strongly the researchers Jing Pu 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. Long Short-Term Memory
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Going deeper with convolutions
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• EIE: Efficient Inference Engine on Compressed Deep Neural Network (2016) — 2,611 citations
• TETRIS: Scalable and Efficient Neural Network Acceleration with 3D Memory (2017) — 561 citations