Yang Lu

Computer Vision and Pattern Recognition · with Wu · 2015–2019

A researcher in the Science corpus: 8 papers; trusts 816 researchers and is trusted by 115 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 190 citations into it; #2,959 of 21,575 by trust here, #4,317 by citations
• Generative Models — 106 citations into it; #249 of 1,956 by trust here, #439 by citations
• Artificial Intelligence — 93 citations into it; #4,055 of 35,345 by trust here, #7,010 by citations
• Probabilistic & Bayesian Methods — 83 citations into it; #1,073 of 1,525 by trust here, #1,075 by citations
• Representation & Self-Supervised Learning — 35 citations into it
• Optimization & Training — 32 citations into it
• Signal Processing — 32 citations into it; #4,804 of 7,572 by trust here, #6,591 by citations
• Model Architectures — 22 citations into it

Whom Yang Lu trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Song-Chun Zhu
5. Yann LeCun
6. Radford M. Neal
7. Yoshua Bengio
8. Ilya Sutskever
9. Ruslan Salakhutdinov
10. Kaiming He

Who trusts Yang Lu overall

1. Jianwen Xie
2. Erik Nijkamp
3. Song-Chun Zhu
4. Ying Nian Wu
5. Mitch Hill
6. Tian Han
7. Yilun Du
8. Ruiqi Gao
9. Tianfu Wu
10. Shuang Li

Papers from Yang Lu's perspective

Ordered by how strongly the researchers Yang 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Theory of Generative ConvNet (2016) — 344 citations
• Cooperative Training of Descriptor and Generator Networks (2016) — 151 citations
• Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching (2018) — 90 citations
• Learning FRAME Models Using CNN Filters (2015) — 67 citations
• Alternating Back-Propagation for Generator Network (2016) — 139 citations
• Learning Generator Networks for Dynamic Patterns (2019) — 10 citations
• Learning Generative ConvNets via Multi-grid Modeling and Sampling (2017) — 82 citations
• Inducing wavelets into random fields via generative boosting (2016) — 23 citations