Gaoliang Peng

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; #2,585 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 Gaoliang Peng trusts overall

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

1. Karen Simonyan
2. Sergey Ioffe
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Achmad Widodo
6. Christian Szegedy
7. Diederik P. Kingma
8. Andrew Zisserman
9. Feng Jia
10. Yoshua Bengio

Who trusts Gaoliang Peng overall

1. Shen Zhang
2. Xiang Li
3. Shibo Zhang
4. Huan Wang
5. Te Han
6. Bingnan Wang
7. Olga Fink
8. Qian Ding
9. Thomas G. Habetler
10. Zhiliang Liu

Papers from Gaoliang Peng's perspective

Ordered by how strongly the researchers Gaoliang Peng 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
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