Jinjiang Wang

Industrial and Manufacturing Engineering · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 13 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Industrial and Manufacturing Engineering — 33 citations into it; #1,994 of 2,521 by trust here, #438 by citations
• Control and Systems Engineering — 32 citations into it; #7,139 of 8,257 by trust here, #1,875 by citations
• Artificial Intelligence — 31 citations into it; #30,881 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Model Architectures — 15 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Natural Language Processing — 7 citations into it

Whom Jinjiang Wang trusts overall

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

1. Geoffrey E. Hinton
2. Ruslan Salakhutdinov
3. Yoshua Bengio
4. Yann LeCun
5. Alex Krizhevsky
6. Kazi Masudul Alam
7. J. Urgen Schmidhuber
8. Fei Tao
9. Ilya Sutskever
10. Radford M. Neal

Who trusts Jinjiang Wang overall

1. Concetta Semeraro
2. Juan Pablo Usuga Cadavid
3. Xinting Yang
4. Zhibin Zhao
5. Niall O' Mahony
6. Meng Ma
7. Mario Lezoche

Papers from Jinjiang Wang's perspective

Ordered by how strongly the researchers Jinjiang Wang 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deep learning for smart manufacturing: Methods and applications (2018) — 1,515 citations
• Digital Twin for rotating machinery fault diagnosis in smart manufacturing (2018) — 463 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