Shuang-Hong Yang

Information Systems · 2011–2013

A researcher in the Science corpus: 4 papers; trusts 35 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 28 citations into it; #3,735 of 8,771 by trust here, #3,315 by citations
• Management Science and Operations Research — 16 citations into it; #5,467 of 11,946 by trust here, #3,825 by citations
• Artificial Intelligence — 15 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it; #11,592 of 21,575 by trust here, #15,529 by citations
• Statistical and Nonlinear Physics — 14 citations into it
• Marketing — 8 citations into it
• Statistical Learning Theory — 7 citations into it
• Recommender Systems — 7 citations into it; #133 of 233 by trust here, #94 by citations

Whom Shuang-Hong Yang trusts overall

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

1. Mark Newman
2. Yehuda Koren
3. Deepak K. Agarwal
4. Jure Leskovec
5. Olav Kallenberg
6. Riley Crane
7. Thomas Hofmann
8. Bee-Chung Chen
9. Manuel Gomez-Rodriguez
10. Matthew R. McLaughlin

Who trusts Shuang-Hong Yang overall

1. Yue Shi
2. Rakshit S. Trivedi
3. Charu C. Aggarwal
4. Qingyuan Zhao
5. Alexandra Olteanu
6. Yongfeng Zhang
7. Weike Pan
8. Aditya Krishna Menon
9. Zi-ke Zhang
10. Wei Gong

Papers from Shuang-Hong Yang's perspective

Ordered by how strongly the researchers Shuang-Hong 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Collaborative competitive filtering: learning recommender using context of user choice (2011) — 159 citations
• Functional matrix factorizations for cold-start recommendation (2011) — 292 citations
• Mixture of Mutually Exciting Processes for Viral Diffusion (2013) — 193 citations
• Like like alike — Joint Friendship and Interest Propagation in Social Networks Shuang (2011)