Seung-Jean Kim

Statistics and Probability · 2005–2009

A researcher in the Science corpus: 6 papers; trusts 49 researchers and is trusted by 91 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 43 citations into it; #8,044 of 12,932 by trust here, #8,279 by citations
• Computational Mechanics — 40 citations into it; #2,598 of 10,001 by trust here, #1,065 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #10,598 of 21,575 by trust here, #12,419 by citations
• Optimization & Training — 28 citations into it
• Artificial Intelligence — 20 citations into it
• Control and Systems Engineering — 17 citations into it; #4,214 of 8,257 by trust here, #3,389 by citations
• Statistical Learning Theory — 15 citations into it
• Management Science and Operations Research — 14 citations into it

Whom Seung-Jean Kim trusts overall

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

1. Robert Tibshirani
2. E.J. Candes
3. Trevor J. Hastie
4. David L. Donoho
5. Stephen J. Wright
6. Eric R. Ziegel
7. Hui Zou
8. Yurii E. Nesterov
9. Ming Yuan
10. Joel A. Tropp

Who trusts Seung-Jean Kim overall

1. Robert Tibshirani
2. Suvrit Sra
3. Stephen J. Wright
4. Meng Yang
5. John C. Duchi
6. Tyler B. Johnson
7. Sebastian Nowozin
8. Stephen P. Boyd
9. Zheng Zhang
10. Lei Zhang

Papers from Seung-Jean Kim's perspective

Ordered by how strongly the researchers Seung-Jean Kim 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. Regression Shrinkage and Selection via the Lasso
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Learning Multiple Layers of Features from Tiny Images
9. Atomic Decomposition by Basis Pursuit
10. Long Short-Term Memory

Their papers

• An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression (2007) — 749 citations
• A tutorial on geometric programming (2007) — 1,250 citations
• Robust Fisher Discriminant Analysis (2005) — 135 citations
• 1 Trend Filtering (2009) — 658 citations
• Distributed average consensus with least-mean-square deviation (2007) — 1,170 citations
• An Interior-Point Method for Large-Scale $\ell_1$-Regularized Least Squares (2007) — 1,886 citations