Weizhong Yan

Artificial Intelligence · 2012–2016

A researcher in the Science corpus: 3 papers; trusts 48 researchers and is trusted by 34 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #5,252 of 35,345 by trust here, #14,438 by citations
• Management Science and Operations Research — 15 citations into it; #1,271 of 11,946 by trust here, #3,457 by citations
• Signal Processing — 11 citations into it; #2,304 of 7,572 by trust here, #3,198 by citations
• Computer Vision and Pattern Recognition — 9 citations into it
• Economics and Econometrics — 8 citations into it
• Model Architectures — 8 citations into it
• Statistics and Probability — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Weizhong Yan trusts overall

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

1. Greg Richards
2. Subhash C. Bagui
3. Ron Kohavi
4. Victor Richmond R. Jose
5. Geoffrey E. Hinton
6. G. Huang
7. Sven F. Crone
8. Imran Maqsood
9. Yoshua Bengio
10. Nesreen K. Ahmed

Who trusts Weizhong Yan overall

1. Kasun Bandara
2. Christoph Bergmeir
3. Md. Mustafizur Rahman
4. Zhaoyi Xu
5. Abebe Abeshu Diro
6. Oludare Isaac Abiodun
7. Slawek Smyl
8. M. Mohammadi
9. Fazle Karim
10. M. Islam

Papers from Weizhong Yan's perspective

Ordered by how strongly the researchers Weizhong Yan 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Time series classification from scratch with deep neural networks: A strong baseline (2016) — 2,081 citations
• Toward Automatic Time-Series Forecasting Using Neural Networks (2012) — 181 citations
• On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach (2015) — 153 citations