Raymond Y. K. Lau

Computer Vision and Pattern Recognition · 2011–2018

A researcher in the Science corpus: 5 papers; trusts 266 researchers and is trusted by 306 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it; #2,325 of 21,575 by trust here, #1,912 by citations
• Generative Models — 60 citations into it; #388 of 1,956 by trust here, #216 by citations
• Artificial Intelligence — 44 citations into it
• Model Architectures — 18 citations into it
• Media Technology — 17 citations into it
• Optimization & Training — 16 citations into it
• Probabilistic & Bayesian Methods — 15 citations into it
• Information Retrieval & Search — 10 citations into it

Whom Raymond Y. K. Lau trusts overall

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

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

Who trusts Raymond Y. K. Lau overall

1. Jun-Yan Zhu
2. Lior Wolf
3. Sagie Benaim
4. Taesung Park
5. Alexia Jolicoeur-Martineau
6. Takuhiro Kaneko
7. Weihao Xia
8. Chris Donahue
9. Ming-Yu Liu
10. Oran Gafni

Papers from Raymond Y. K. Lau's perspective

Ordered by how strongly the researchers Raymond Y. K. Lau 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. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Least Squares Generative Adversarial Networks (2016) — 5,091 citations
• On the Effectiveness of Least Squares Generative Adversarial Networks (2017) — 171 citations
• Hyperspectral Image Classification With Deep Learning Models (2018) — 435 citations
• Multi-class Generative Adversarial Networks with the L2 Loss Function (2016) — 222 citations
• Toward a semantic granularity model for domain-specific information retrieval (2011) — 57 citations