Zhen Wang

Computer Vision and Pattern Recognition · with Lau · 2016–2017

A researcher in the Science corpus: 3 papers; trusts 227 researchers and is trusted by 306 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 67 citations into it; #2,329 of 21,575 by trust here, #1,964 by citations
• Generative Models — 60 citations into it; #390 of 1,956 by trust here, #216 by citations
• Artificial Intelligence — 33 citations into it
• Probabilistic & Bayesian Methods — 15 citations into it
• Optimization & Training — 13 citations into it
• Model Architectures — 12 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Media Technology — 5 citations into it

Whom Zhen Wang trusts overall

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

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

Who trusts Zhen Wang 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

Papers from Zhen Wang's perspective

Ordered by how strongly the researchers Zhen Wang 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Least Squares Generative Adversarial Networks (2016) — 5,091 citations
• On the Effectiveness of Least Squares Generative Adversarial Networks (2017) — 171 citations
• Multi-class Generative Adversarial Networks with the L2 Loss Function (2016) — 222 citations