Ye Xiang

Computer Vision and Pattern Recognition · with Fu · 2019

A researcher in the Science corpus: 1 paper; trusts 121 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 26 citations into it; #9,649 of 21,575 by trust here, #8,831 by citations
• Artificial Intelligence — 24 citations into it; #14,974 of 35,345 by trust here, #12,761 by citations
• Generative Models — 10 citations into it; #905 of 1,956 by trust here, #1,010 by citations
• Meta & Continual Learning — 10 citations into it; #457 of 1,041 by trust here, #443 by citations
• Model Architectures — 7 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Ye Xiang trusts overall

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

1. Kaiming He
2. Karen Simonyan
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Christian Szegedy
7. Andrew Zisserman
8. Diederik P. Kingma
9. Yann LeCun
10. Augustus Odena

Who trusts Ye Xiang overall

1. Jiangpeng He
2. Hongjoon Ahn
3. Fei Zhu
4. Feng Zhu
5. Ali Ayub
6. Da-Wei Zhou
7. Xialei Liu
8. Julien Pourcel
9. Zhiwei Lin
10. Youngmin Oh

Papers from Ye Xiang's perspective

Ordered by how strongly the researchers Ye Xiang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Incremental Learning Using Conditional Adversarial Networks (2019) — 193 citations