Xudong Mao

Computer Vision and Pattern Recognition · 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; #2,330 of 21,575 by trust here, #1,964 by citations
• Generative Models — 60 citations into it; #391 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 Xudong Mao trusts overall

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

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

Who trusts Xudong Mao overall

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

Papers from Xudong Mao's perspective

Ordered by how strongly the researchers Xudong Mao 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge

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