Chuan Li

Control and Systems Engineering · with Sánchez · 2015–2020

A researcher in the Science corpus: 4 papers; trusts 10 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 56 citations into it; #6,512 of 8,257 by trust here, #1,718 by citations
• Mechanical Engineering — 35 citations into it
• Mechanics of Materials — 28 citations into it; #4,753 of 6,291 by trust here, #1,848 by citations
• Artificial Intelligence — 21 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Representation & Self-Supervised Learning — 13 citations into it

Whom Chuan Li trusts overall

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

1. Yoshua Bengio
2. Chih-Jen Lin
3. Geoffrey E. Hinton
4. Yann LeCun
5. David H. Hubel
6. Alex Krizhevsky
7. Ruqiang Yan
8. Prasanna Tamilselvan
9. Ilya Sutskever
10. Radford M. Neal

Who trusts Chuan Li overall

1. Luyang Jing
2. Ki Bum Lee
3. Haidong Shao
4. Jinyang Jiao
5. David Verstraete

Papers from Chuan Li's perspective

Ordered by how strongly the researchers Chuan Li trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Gradient-based learning applied to document recognition
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A systematic review of deep transfer learning for machinery fault diagnosis (2020) — 414 citations
• A review on data-driven fault severity assessment in rolling bearings (2018) — 645 citations
• Fault diagnosis in spur gears based on genetic algorithm and random forest (2016) — 350 citations
• Gearbox Fault Identification and Classification with Convolutional Neural Networks (2015) — 407 citations