Shuheng Zhou

Artificial Intelligence · 2007–2011

A researcher in the Science corpus: 6 papers; trusts 70 researchers and is trusted by 64 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #3,475 of 35,345 by trust here, #7,346 by citations
• Statistics and Probability — 59 citations into it; #870 of 12,932 by trust here, #1,130 by citations
• Computational Mechanics — 46 citations into it; #9,052 of 10,001 by trust here, #8,258 by citations
• Privacy & Security — 27 citations into it; #100 of 709 by trust here, #232 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Computational Theory and Mathematics — 16 citations into it; #3,422 of 12,182 by trust here, #8,756 by citations
• Applied Mathematics — 15 citations into it; #2,299 of 7,620 by trust here, #5,097 by citations
• Computer Networks and Communications — 14 citations into it; #8,940 of 9,886 by trust here, #8,191 by citations

Whom Shuheng Zhou trusts overall

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

1. Robert Tibshirani
2. Cynthia Dwork
3. Martin J. Wainwright
4. Joel A. Tropp
5. E.J. Candes
6. David L. Donoho
7. Frank McSherry
8. Kobbi Nissim
9. Nicolai Meinshausen
10. Scott Shaobing Chen

Who trusts Shuheng Zhou overall

1. John C. Duchi
2. Kamalika Chaudhuri
3. Martin J. Wainwright
4. Daniel Kifer
5. Anand D. Sarwate
6. Adam D. Smith
7. Michael I. Jordan
8. Garvesh Raskutti
9. Sahand N. Negahban
10. Cynthia Dwork

Papers from Shuheng Zhou's perspective

Ordered by how strongly the researchers Shuheng Zhou trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• A Statistical Framework for Differential Privacy (2008) — 565 citations
• Differential privacy with compression (2009) — 67 citations
• The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso) (2011) — 142 citations
• Restricted Eigenvalue Conditions on Subgaussian Random Matrices (2009) — 86 citations
• Compressed and Privacy-Sensitive Sparse Regression (2009) — 68 citations
• Compressed Regression (2007) — 52 citations