Jianqiao Sun

Control and Systems Engineering · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 64 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 34 citations into it; #1,220 of 8,257 by trust here, #1,592 by citations
• Artificial Intelligence — 29 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Mechanical Engineering — 15 citations into it; #736 of 8,300 by trust here, #3,348 by citations
• Mechanics of Materials — 15 citations into it; #4,832 of 6,291 by trust here, #4,968 by citations
• Representation & Self-Supervised Learning — 11 citations into it
• Optimization & Training — 10 citations into it
• Model Architectures — 8 citations into it

Whom Jianqiao Sun 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. Christian Szegedy
8. Ilya Sutskever
9. Yann LeCun
10. Oriol Vinyals

Who trusts Jianqiao Sun overall

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

Papers from Jianqiao Sun's perspective

Ordered by how strongly the researchers Jianqiao Sun 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. Going deeper with convolutions
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Multi-Layer domain adaptation method for rolling bearing fault diagnosis (2019) — 435 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