Te Han

Control and Systems Engineering · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 15 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 37 citations into it
• Mechanical Engineering — 23 citations into it
• Artificial Intelligence — 17 citations into it
• Mechanics of Materials — 14 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Representation & Self-Supervised Learning — 9 citations into it

Whom Te Han trusts overall

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

1. Mingsheng Long
2. Leo Breiman
3. Yaguo Lei
4. Chih-Jen Lin
5. Tin Kam Ho
6. Sinno Jialin Pan
7. Jianmin Wang
8. Long Wen
9. Yoshua Bengio
10. Geoffrey E. Hinton

Who trusts Te Han overall

1. Long Wen
2. Zhibin Zhao
3. Rui Yang
4. Yan Xu
5. Xinyu Li
6. Yihao Xue
7. Yanming Sun
8. Liang Gao
9. Qiyang Zhang

Papers from Te Han's perspective

Ordered by how strongly the researchers Te Han 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. Gradient-based learning applied to document 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
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 novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults (2019) — 448 citations
• Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application (2018) — 513 citations
• Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery (2018) — 341 citations