Weining Lu

Computer Vision and Pattern Recognition · 2014–2017

A researcher in the Science corpus: 4 papers; trusts 151 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 46 citations into it; #14,785 of 21,575 by trust here, #15,529 by citations
• Artificial Intelligence — 39 citations into it; #15,865 of 35,345 by trust here, #10,430 by citations
• Model Architectures — 16 citations into it
• Representation & Self-Supervised Learning — 16 citations into it; #2,374 of 3,145 by trust here, #2,066 by citations
• Aerospace Engineering — 11 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it
• Control and Systems Engineering — 8 citations into it; #7,301 of 8,257 by trust here, #3,004 by citations
• Optimization & Training — 7 citations into it

Whom Weining Lu trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Yann LeCun
4. Alex Krizhevsky
5. David G. Lowe
6. Mingsheng Long
7. Pascal Vincent
8. Kaiming He
9. Ilya Sutskever
10. Diederik P. Kingma

Who trusts Weining Lu overall

1. Xiang Li
2. Liangjun Feng
3. Long Wen
4. Paulo da Costa
5. Qian Ding
6. Liang Guo
7. Rui Yang
8. Zhibin Zhao
9. Yaguo Lei
10. Houssam Zenati

Papers from Weining Lu's perspective

Ordered by how strongly the researchers Weining 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. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep Structured Energy Based Models for Anomaly Detection (2016) — 463 citations
• Doubly Convolutional Neural Networks (2016) — 66 citations
• Deep Model Based Domain Adaptation for Fault Diagnosis (2017) — 679 citations
• Vision-Based Pose Estimation From Points With Unknown Correspondences (2014) — 25 citations