Xiang Li

Control and Systems Engineering · Reliability Engineering & System Safety · 2018–2020

A researcher in the Science corpus: 8 papers; trusts 155 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 92 citations into it; #600 of 8,257 by trust here, #688 by citations
• Artificial Intelligence — 59 citations into it
• Mechanical Engineering — 42 citations into it; #349 of 8,300 by trust here, #2,138 by citations
• Mechanics of Materials — 37 citations into it; #1,797 of 6,291 by trust here, #2,279 by citations
• Computer Vision and Pattern Recognition — 36 citations into it
• Optimization & Training — 24 citations into it
• Representation & Self-Supervised Learning — 24 citations into it
• Model Architectures — 14 citations into it

Whom Xiang Li 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. Oriol Vinyals
8. Ilya Sutskever
9. Arthur Gretton
10. Yann LeCun

Who trusts Xiang Li overall

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

Papers from Xiang Li's perspective

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

Their papers

• Machinery fault diagnosis with imbalanced data using deep generative adversarial networks (2020) — 328 citations
• 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