Qian Ding

Control and Systems Engineering · Reliability Engineering & System Safety · 2018–2019

A researcher in the Science corpus: 7 papers; trusts 155 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 82 citations into it; #608 of 8,257 by trust here, #747 by citations
• Artificial Intelligence — 54 citations into it
• Computer Vision and Pattern Recognition — 36 citations into it
• Mechanical Engineering — 36 citations into it; #354 of 8,300 by trust here, #2,239 by citations
• Mechanics of Materials — 32 citations into it; #1,796 of 6,291 by trust here, #2,279 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Optimization & Training — 23 citations into it
• Model Architectures — 14 citations into it

Whom Qian Ding trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Xavier Glorot
4. Alex Krizhevsky
5. David E. Rumelhart
6. Kaiming He
7. Ilya Sutskever
8. Yann LeCun
9. Christian Szegedy
10. Oriol Vinyals

Who trusts Qian Ding overall

1. Jinyang Jiao
2. Dhiraj Neupane
3. Ming Zhao
4. Paulo da Costa
5. Manuel Arias Chao
6. Xiang Li
7. Olga Fink
8. Zhibin Zhao
9. Zhaoyi Xu
10. Jing Lin

Papers from Qian Ding's perspective

Ordered by how strongly the researchers Qian Ding 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. 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. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction (2019) — 512 citations
• Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism (2019) — 346 citations
• Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks (2019) — 350 citations
• Multi-Layer domain adaptation method for rolling bearing fault diagnosis (2019) — 435 citations
• Deep residual learning-based fault diagnosis method for rotating machinery. (2019) — 367 citations
• Remaining useful life estimation in prognostics using deep convolution neural networks (2018) — 1,516 citations
• Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation (2018) — 367 citations