Jing Lin

Control and Systems Engineering · with Zhang · 2020–2023

A researcher in the Science corpus: 2 papers; trusts 26 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 64 citations into it
• Mechanical Engineering — 38 citations into it
• Artificial Intelligence — 17 citations into it
• Mechanics of Materials — 16 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Model Architectures — 7 citations into it
• Optimization & Training — 5 citations into it

Whom Jing Lin trusts overall

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

1. Xiang Li
2. Geoffrey E. Hinton
3. Duy-Tang Hoang
4. Qian Ding
5. Kaiming He
6. Andrew L. Maas
7. Yaguo Lei
8. Sergey Ioffe
9. Alex Krizhevsky
10. Vinod Nair

Papers from Jing Lin's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. GENERATIVE ADVERSARIAL NETS
7. Dropout: a simple way to prevent neural networks from overfitting
8. Long Short-Term Memory
9. Rectified Linear Units Improve Restricted Boltzmann Machines
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Feature Mode Decomposition: New Decomposition Theory for Rotating Machinery Fault Diagnosis (2023) — 338 citations
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020) — 514 citations