Guo-Xun Yuan

Artificial Intelligence · 2010–2012

A researcher in the Science corpus: 3 papers; trusts 110 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 75 citations into it; #5,058 of 35,345 by trust here, #10,667 by citations
• Computer Vision and Pattern Recognition — 52 citations into it; #21,441 of 21,575 by trust here, #14,478 by citations
• Optimization & Training — 50 citations into it
• Computational Mechanics — 45 citations into it; #9,897 of 10,001 by trust here, #5,157 by citations
• Statistics and Probability — 29 citations into it; #2,596 of 12,932 by trust here, #5,857 by citations
• Statistical Learning Theory — 16 citations into it
• Mathematical Optimization — 12 citations into it
• Numerical Analysis — 10 citations into it

Whom Guo-Xun Yuan trusts overall

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

1. Chih-Jen Lin
2. Shai Shalev-Shwartz
3. Robert Tibshirani
4. Jerome H. Friedman
5. Thorsten Joachims
6. Trevor J. Hastie
7. Yurii E. Nesterov
8. L. Eon Bottou
9. Paul Tseng
10. N. Schraudolph

Who trusts Guo-Xun Yuan overall

1. Virginia Smith
2. Ilya Trofimov
3. Simone Forte
4. Dhruv Kumar Mahajan
5. Celestine Mendler-Dünner
6. Kimon Fountoulakis
7. A. Genkin
8. An Bian
9. Inderjit S. Dhillon
10. Martin Takác

Papers from Guo-Xun Yuan's perspective

Ordered by how strongly the researchers Guo-Xun Yuan 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Comparison of Optimization Methods and Software for Large-scale L1-regularized Linear Classification (2010) — 261 citations
• An improved GLMNET for l1-regularized logistic regression (2011) — 244 citations
• Recent Advances of Large-Scale Linear Classification (2012) — 306 citations