Xiangyang Lan

Statistical and Nonlinear Physics · 2005–2010

A researcher in the Science corpus: 4 papers; trusts 44 researchers and is trusted by 163 researchers here, as inferred from the citation graph.

Subfields

• Statistical and Nonlinear Physics — 26 citations into it; #3,324 of 10,127 by trust here, #954 by citations
• Computer Vision and Pattern Recognition — 17 citations into it; #2,802 of 21,575 by trust here, #10,500 by citations
• Artificial Intelligence — 10 citations into it; #24,620 of 35,345 by trust here, #28,481 by citations
• Sociology and Political Science — 7 citations into it
• Computer Networks and Communications — 5 citations into it; #3,570 of 9,886 by trust here, #973 by citations
• Media Technology — 5 citations into it; #2,354 of 3,621 by trust here, #2,820 by citations

Whom Xiangyang Lan trusts overall

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

1. Pedro F. Felzenszwalb
2. Geoffrey E. Hinton
3. Sergey Ioffe
4. Deva Ramanan
5. Max Welling Donald
6. D.A. Forsyth
7. Dan Huttenlocher
8. Stefan Roth
9. Stuart Geman
10. Yuri Boykov

Who trusts Xiangyang Lan overall

1. Leonid Sigal
2. Marcin Eichner
3. Zhe Cao
4. Michael J. Black
5. Vittorio Ferrari
6. Hiroshi Ishikawa
7. Stefan Roth
8. Neil D. Lawrence
9. Yuandong Tian
10. Benjamin Sapp

Papers from Xiangyang Lan's perspective

Ordered by how strongly the researchers Xiangyang Lan 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Beyond trees: common-factor models for 2D human pose recovery (2005) — 189 citations
• Efficient Belief Propagation with Learned Higher-Order Markov Random Fields (2006) — 225 citations
• Sequential Influence Models in Social Networks (2010) — 88 citations
• Group formation in large social networks: membership, growth, and evolution (2006) — 2,119 citations