Jianguo Li

Computer Vision and Pattern Recognition · with Liu · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 425 researchers and is trusted by 128 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 117 citations into it; #3,022 of 21,575 by trust here, #2,033 by citations
• Artificial Intelligence — 101 citations into it; #10,164 of 35,345 by trust here, #5,187 by citations
• Efficiency & Compression — 43 citations into it; #405 of 1,264 by trust here, #264 by citations
• Model Architectures — 42 citations into it
• Optimization & Training — 25 citations into it
• Safety, Robustness & Interpretability — 12 citations into it; #922 of 2,251 by trust here, #692 by citations
• Computational Mechanics — 7 citations into it
• Representation & Self-Supervised Learning — 7 citations into it

Whom Jianguo Li trusts overall

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

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

Who trusts Jianguo Li overall

1. Jiahui Yu
2. Yang He
3. Han Cai
4. Zhuang Liu
5. Zhiqiang Shen
6. Tianhong Li
7. Ji Lin
8. Thomas S. Huang
9. Haoli Bai
10. Muyang Li

Papers from Jianguo Li's perspective

Ordered by how strongly the researchers Jianguo Li 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Learning Efficient Convolutional Networks through Network Slimming (2017) — 2,811 citations
• DSOD: Learning Deeply Supervised Object Detectors from Scratch (2017) — 628 citations
• Boosting Adversarial Attacks with Momentum (2017) — 3,379 citations
• Supplementary Material for Few Sample Knowledge Distillation for Efficient Network Compression (2020)
• Knowledge Distillation from Few Samples (2018) — 15 citations
• Network Decoupling: From Regular to Depthwise Separable Convolutions (2018) — 100 citations