Rui Zhao

Control and Systems Engineering · IEEE transactions on industrial electronics (1982. Print) · 2016–2021

A researcher in the Science corpus: 5 papers; trusts 25 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 57 citations into it; #4,059 of 8,257 by trust here, #946 by citations
• Artificial Intelligence — 44 citations into it; #30,131 of 35,345 by trust here, #10,319 by citations
• Computer Vision and Pattern Recognition — 33 citations into it
• Mechanical Engineering — 28 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Model Architectures — 22 citations into it
• Mechanics of Materials — 20 citations into it
• Natural Language Processing — 10 citations into it

Whom Rui Zhao trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. J. Urgen Schmidhuber
4. Sepp Hochreiter
5. Yann LeCun
6. Alex Krizhevsky
7. Diederik P. Kingma
8. Vinod Nair
9. L. V. D. van der Maaten
10. Ilya Sutskever

Who trusts Rui Zhao overall

1. Meng Ma
2. Long Wen
3. Ruqiang Yan
4. Mohsen Azimi
5. Olga Fink
6. Weiting Zhang
7. Saikat Chakraborty
8. Shaobo Li
9. Zhu Mao
10. Laila Rasmy

Papers from Rui Zhao's perspective

Ordered by how strongly the researchers Rui Zhao 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Machine Remaining Useful Life Prediction via an Attention-Based Deep Learning Approach (2021) — 521 citations
• Deep learning and its applications to machine health monitoring (2019) — 2,506 citations
• Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks (2018) — 735 citations
• Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks (2017) — 691 citations
• A sparse auto-encoder-based deep neural network approach for induction motor faults classification (2016) — 675 citations