Ming Zhao

Control and Systems Engineering · with Jia · 2017–2020

A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 77 citations into it
• Mechanical Engineering — 41 citations into it
• Artificial Intelligence — 30 citations into it
• Mechanics of Materials — 23 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Model Architectures — 8 citations into it
• Optimization & Training — 7 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Ming Zhao trusts overall

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

1. Xiang Li
2. Geoffrey E. Hinton
3. Duy-Tang Hoang
4. Yaguo Lei
5. Kaiming He
6. Qian Ding
7. Alex Krizhevsky
8. Yoshua Bengio
9. Sergey Ioffe
10. Yann LeCun

Who trusts Ming Zhao overall

1. Jinyang Jiao
2. Te Han

Papers from Ming Zhao's perspective

Ordered by how strongly the researchers Ming Zhao 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Going deeper with convolutions
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A comprehensive review on convolutional neural network in machine fault diagnosis (2020) — 514 citations
• A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery (2017) — 286 citations
• A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox (2017) — 640 citations