Yuan Shi

Artificial Intelligence · 2012

A researcher in the Science corpus: 2 papers; trusts 110 researchers and is trusted by 843 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #1,558 of 35,345 by trust here, #3,091 by citations
• Representation & Self-Supervised Learning — 27 citations into it; #276 of 3,145 by trust here, #460 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #1,499 of 21,575 by trust here, #2,562 by citations
• Evaluation & Benchmarks — 7 citations into it

Whom Yuan Shi trusts overall

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

1. John Blitzer
2. Geoffrey E. Hinton
3. Christopher Evans
4. Yoshua Bengio
5. Kate Saenko
6. Sinno Jialin Pan
7. Sam T. Roweis
8. Kilian Q. Weinberger
9. Raghuraman Gopalan
10. Alex Krizhevsky

Who trusts Yuan Shi overall

1. Mingsheng Long
2. Judy Hoffman
3. Jian Liang
4. Wouter M. Kouw
5. Vishal M. Patel
6. Wen Li
7. Zhangjie Cao
8. Jianmin Wang
9. Kate Saenko
10. Hui Tang

Papers from Yuan Shi's perspective

Ordered by how strongly the researchers Yuan Shi 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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

• Geodesic flow kernel for unsupervised domain adaptation (2012) — 2,444 citations
• Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation (2012) — 252 citations