Jing Yang

Computer Vision and Pattern Recognition · with Abbaspour · 2007–2020

A researcher in the Science corpus: 9 papers; trusts 411 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 65 citations into it; #13,130 of 21,575 by trust here, #13,815 by citations
• Artificial Intelligence — 35 citations into it
• Generative Models — 17 citations into it
• Water Science and Technology — 17 citations into it; #382 of 1,711 by trust here, #756 by citations
• Efficiency & Compression — 16 citations into it
• Finance — 15 citations into it; #5,889 of 10,518 by trust here, #2,779 by citations
• Environmental Engineering — 13 citations into it; #222 of 5,479 by trust here, #1,360 by citations
• Media Technology — 13 citations into it

Whom Jing Yang trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Qingyun Duan
5. Karen Simonyan
6. Keith Beven
7. Diederik P. Kingma
8. Yoshua Bengio
9. Yann LeCun
10. Sergey Zagoruyko

Who trusts Jing Yang overall

1. H. Dai
2. A. Nguyen
3. Fanny Sarrazin
4. Xuesong Zhang
5. Laura Uusitalo
6. Jianyong Wu
7. Francesca Pianosi
8. Lu Li
9. Xiaoli Jin
10. Chong-yu Xu

Papers from Jing Yang's perspective

Ordered by how strongly the researchers Jing Yang 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• To learn image super-resolution, use a GAN to learn how to do image degradation first (2018) — 402 citations
• Network Models and Financial Stability (2007) — 980 citations
• Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis (2011) — 333 citations
• Knowledge distillation via adaptive instance normalization (2020) — 29 citations
• Financial Structure and Growth (2014) — 147 citations
• Chinese Prosody Structure Prediction Based on Conditional Random Fields (2009) — 6 citations
• Complete solving of explicit evaluation of Gauss sums in the index 2 case (2009) — 54 citations
• Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China (2008) — 790 citations
• Hydrological modelling of the Chaohe Basin in China: Statistical model formulation and Bayesian inference (2007) — 248 citations