Haiping Lu

Computer Vision and Pattern Recognition · IEEE Transactions on Neural Networks · 2008–2022

A researcher in the Science corpus: 4 papers; trusts 94 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 42 citations into it; #7,911 of 21,575 by trust here, #11,950 by citations
• Signal Processing — 24 citations into it; #2,048 of 7,572 by trust here, #2,743 by citations
• Artificial Intelligence — 23 citations into it
• Computational Theory and Mathematics — 20 citations into it
• Computational Mathematics — 17 citations into it; #377 of 612 by trust here, #372 by citations
• Cognitive Neuroscience — 10 citations into it; #9,353 of 10,852 by trust here, #10,284 by citations
• Biomedical Engineering — 7 citations into it; #2,701 of 10,734 by trust here, #7,854 by citations
• Computational Mechanics — 5 citations into it

Whom Haiping Lu trusts overall

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

1. Jieping Ye
2. M. Alex O. Vasilescu
3. Shuicheng Yan
4. Tamara G. Kolda
5. Arun A. Ross
6. Demetri Terzopoulos
7. Lieven De Lathauwer
8. Matthew A. Turk
9. Benjamin Blankertz
10. Jian Yang

Who trusts Haiping Lu overall

1. Jiwen Lu
2. Fabien Lotte
3. Andrzej S. Cichocki
4. Chengcheng Jia
5. Qun Li
6. Philip Chen
7. Guoxu Zhou
8. Xian Guo
9. Wen-Jing Yan
10. Yap-Peng Tan

Papers from Haiping Lu's perspective

Ordered by how strongly the researchers Haiping Lu 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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• MPCA: Multilinear Principal Component Analysis of Tensor Objects (2008) — 907 citations
• Interpretable bilinear attention network with domain adaptation improves drug–target prediction (2022) — 336 citations
• A survey of multilinear subspace learning for tensor data (2011) — 380 citations
• Regularized Common Spatial Pattern With Aggregation for EEG Classification in Small-Sample Setting (2010) — 316 citations