Hongkai Jiang

Control and Systems Engineering · 2015–2018

A researcher in the Science corpus: 6 papers; trusts 28 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 51 citations into it; #1,351 of 8,257 by trust here, #549 by citations
• Mechanical Engineering — 30 citations into it; #1,045 of 8,300 by trust here, #2,016 by citations
• Artificial Intelligence — 20 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Mechanics of Materials — 19 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Model Architectures — 7 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Hongkai Jiang trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Yaguo Lei
4. Yann LeCun
5. Honglak Lee
6. Prasanna Tamilselvan
7. Alex Krizhevsky
8. J. Urgen Schmidhuber
9. Jianbo Yu
10. Dong Yu

Who trusts Hongkai Jiang overall

1. Shen Zhang
2. Shibo Zhang
3. Haidong Shao
4. Long Wen
5. Weiting Zhang
6. Bingnan Wang
7. Dhiraj Neupane
8. Zhuyun Chen
9. Thomas G. Habetler
10. Yifei Ding

Papers from Hongkai Jiang's perspective

Ordered by how strongly the researchers Hongkai Jiang 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing (2018) — 349 citations
• Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network (2018) — 434 citations
• A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders (2018) — 434 citations
• An enhancement deep feature fusion method for rotating machinery fault diagnosis (2017) — 293 citations
• A novel deep autoencoder feature learning method for rotating machinery fault diagnosis (2017) — 630 citations
• Rolling bearing fault diagnosis using an optimization deep belief network (2015) — 495 citations