Jun Liu

Optimization & Training · with Ye · 2009–2013

A researcher in the Science corpus: 9 papers; trusts 97 researchers and is trusted by 75 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 54 citations into it; #1,130 of 2,601 by trust here, #1,349 by citations
• Statistics and Probability — 50 citations into it; #3,933 of 12,932 by trust here, #5,481 by citations
• Artificial Intelligence — 45 citations into it; #3,393 of 35,345 by trust here, #6,157 by citations
• Computational Mechanics — 45 citations into it; #1,012 of 10,001 by trust here, #1,651 by citations
• Computer Vision and Pattern Recognition — 26 citations into it; #3,840 of 21,575 by trust here, #5,890 by citations
• Molecular Biology — 23 citations into it; #5,507 of 17,773 by trust here, #14,833 by citations
• Representation & Self-Supervised Learning — 22 citations into it
• Mathematical Optimization — 16 citations into it

Whom Jun Liu trusts overall

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

1. Yurii E. Nesterov
2. Robert Tibshirani
3. Arkadi Nemirovski
4. J. Moreau
5. Jerome H. Friedman
6. Ming Yuan
7. Shuiwang Ji
8. Paul Tseng
9. Jieping Ye
10. Trevor J. Hastie

Who trusts Jun Liu overall

1. Jieping Ye
2. Pinghua Gong
3. Yu Zhang
4. Lei Han
5. Jiayu Zhou
6. Shuiwang Ji
7. Lei Yuan
8. Jie Wang
9. Xiaoyang Tan
10. Qiang Yang

Papers from Jun Liu's perspective

Ordered by how strongly the researchers Jun Liu 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. ImageNet: A large-scale hierarchical image database
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. Regression Shrinkage and Selection via the Lasso
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization (2009) — 755 citations
• Large-scale sparse logistic regression (2009) — 225 citations
• An efficient algorithm for a class of fused lasso problems (2010) — 216 citations
• Efficient Methods for Overlapping Group Lasso (2011) — 235 citations
• Modeling disease progression via multi-task learning (2013) — 208 citations
• SLEP: Sparse Learning with Efficient Projections (2011) — 698 citations
• A multi-task learning formulation for predicting disease progression (2011) — 263 citations
• Moreau-Yosida Regularization for Grouped Tree Structure Learning (2010) — 191 citations
• A convex formulation for learning shared structures from multiple tasks (2009) — 210 citations