Yuanhang Chen

Control and Systems Engineering · 2017–2020

A researcher in the Science corpus: 4 papers; trusts 13 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 35 citations into it; #2,586 of 8,257 by trust here, #1,038 by citations
• Artificial Intelligence — 34 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it
• Model Architectures — 16 citations into it
• Optimization & Training — 9 citations into it
• Signal Processing — 7 citations into it

Whom Yuanhang Chen trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Diederik P. Kingma
5. Karen Simonyan
6. Sergey Ioffe
7. Ilya Sutskever
8. Yann LeCun
9. Christian Szegedy
10. Kaiming He

Who trusts Yuanhang Chen overall

1. Shen Zhang
2. Xiang Li
3. Shibo Zhang
4. Huan Wang

Papers from Yuanhang Chen's perspective

Ordered by how strongly the researchers Yuanhang Chen 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. Gradient-based learning applied to document recognition
4. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A novel deep learning method based on attention mechanism for bearing remaining useful life prediction (2020) — 331 citations
• A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis (2019) — 327 citations
• A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load (2018) — 1,261 citations
• A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals (2017) — 1,600 citations