Jianhui Chen

Artificial Intelligence · 2009–2011

A researcher in the Science corpus: 5 papers; trusts 53 researchers and is trusted by 72 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #3,802 of 35,345 by trust here, #7,310 by citations
• Representation & Self-Supervised Learning — 35 citations into it; #994 of 3,145 by trust here, #1,913 by citations
• Computational Mechanics — 22 citations into it; #415 of 10,001 by trust here, #617 by citations
• Computer Vision and Pattern Recognition — 20 citations into it; #6,062 of 21,575 by trust here, #14,151 by citations
• Optimization & Training — 18 citations into it; #621 of 2,601 by trust here, #804 by citations
• Computer Networks and Communications — 10 citations into it
• Statistical Learning Theory — 9 citations into it
• Statistics and Probability — 9 citations into it

Whom Jianhui Chen trusts overall

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

1. Jonathan Baxter
2. Arkadi Nemirovski
3. R. Caruana
4. Yurii E. Nesterov
5. Andreas Argyriou
6. Theodoros Evgeniou
7. Bart J. Bakker
8. Kai Yu
9. Chih-Jen Lin
10. Neil D. Lawrence

Who trusts Jianhui Chen overall

1. Yu Zhang
2. Yu Zhang
3. Qiang Yang
4. Lei Han
5. Jiayu Zhou
6. Jieping Ye
7. Pinghua Gong
8. Jianpeng Xu
9. Leon Wenliang Zhong
10. Xiao-Lei Zhang

Papers from Jianhui Chen's perspective

Ordered by how strongly the researchers Jianhui Chen 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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. 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

• Large-scale sparse logistic regression (2009) — 225 citations
• Integrating low-rank and group-sparse structures for robust multi-task learning (2011) — 344 citations
• Learning incoherent sparse and low-rank patterns from multiple tasks (2010) — 162 citations
• Clustered Multi-Task Learning Via Alternating Structure Optimization (2011) — 320 citations
• A convex formulation for learning shared structures from multiple tasks (2009) — 210 citations