Ruonan Liu

Control and Systems Engineering · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 30 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 46 citations into it; #3,414 of 8,257 by trust here, #1,333 by citations
• Mechanical Engineering — 28 citations into it; #5,088 of 8,300 by trust here, #4,032 by citations
• Artificial Intelligence — 24 citations into it
• Mechanics of Materials — 21 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Civil and Structural Engineering — 13 citations into it
• Optimization & Training — 6 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Ruonan Liu trusts overall

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

1. Yann LeCun
2. Yoshua Bengio
3. David H. Hubel
4. Vladimir Naumovich Vapni
5. Christopher K. I. Williams
6. Geoffrey E. Hinton
7. L. Eon Bottou
8. Diederik P. Kingma
9. Yaguo Lei
10. Alex Krizhevsky

Who trusts Ruonan Liu overall

1. Shen Zhang
2. Jinyang Jiao
3. Shibo Zhang
4. Long Wen
5. Huan Wang
6. Guoqian Jiang
7. Mikel Canizo
8. Boyuan Yang
9. Zhibin Zhao
10. Bingnan Wang

Papers from Ruonan Liu's perspective

Ordered by how strongly the researchers Ruonan Liu 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Multiscale Kernel Based Residual Convolutional Neural Network for Motor Fault Diagnosis Under Nonstationary Conditions (2020) — 358 citations
• Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture (2019) — 341 citations
• Artificial intelligence for fault diagnosis of rotating machinery: A review (2018) — 1,920 citations
• Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine (2017) — 316 citations
• The application of cubic B-spline collocation method in impact force identification (2015) — 85 citations