Duy-Tang Hoang

Control and Systems Engineering · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 13 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Control and Systems Engineering — 36 citations into it; #3,576 of 8,257 by trust here, #4,962 by citations
• Artificial Intelligence — 27 citations into it
• Mechanical Engineering — 22 citations into it; #2,708 of 8,300 by trust here, #6,237 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Model Architectures — 10 citations into it
• Analytical Chemistry — 6 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it
• Signal Processing — 5 citations into it

Whom Duy-Tang Hoang trusts overall

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

1. Li Deng
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Yann LeCun
5. Ruslan Salakhutdinov
6. Alex Krizhevsky
7. Jing Tian
8. Ilya Sutskever
9. Hugo Larochelle
10. J. Urgen Schmidhuber

Who trusts Duy-Tang Hoang overall

1. Jinyang Jiao
2. Ming Zhao
3. Jing Lin
4. Kaixuan Liang

Papers from Duy-Tang Hoang's perspective

Ordered by how strongly the researchers Duy-Tang Hoang 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Motor Current Signal-Based Bearing Fault Diagnosis Using Deep Learning and Information Fusion (2020) — 324 citations
• Rolling element bearing fault diagnosis using convolutional neural network and vibration image (2019) — 420 citations
• A survey on Deep Learning based bearing fault diagnosis (2019) — 769 citations