Chao Shang

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 2 papers; trusts 39 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it; #33,328 of 35,345 by trust here, #20,377 by citations
• Graph Learning — 22 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Molecular Biology — 11 citations into it
• Model Architectures — 9 citations into it
• Signal Processing — 6 citations into it; #7,191 of 7,572 by trust here, #6,083 by citations
• Management Science and Operations Research — 5 citations into it
• Natural Language Processing — 5 citations into it

Whom Chao Shang trusts overall

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

1. Yann LeCun
2. Ilya Sutskever
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Sepp Hochreiter
6. Diederik P. Kingma
7. Alex Krizhevsky
8. J. Urgen Schmidhuber
9. Oriol Vinyals
10. S. Brandt

Who trusts Chao Shang overall

1. Zezhi Shao
2. Katsuhiko Ishiguro
3. Guangyin Jin
4. José Jiménez-Luna
5. Konstantinos Benidis
6. Zhao Zhang
7. Tomasz Danel
8. Shin-ichi Maeda
9. Fei Wang
10. Yuxuan Liang

Papers from Chao Shang's perspective

Ordered by how strongly the researchers Chao Shang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Edge Attention-based Multi-Relational Graph Convolutional Networks (2018) — 96 citations
• Discrete Graph Structure Learning for Forecasting Multiple Time Series (2021) — 377 citations