Guang-Tong Zhou

Artificial Intelligence · 2008–2018

A researcher in the Science corpus: 2 papers; trusts 82 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 38 citations into it
• Computer Vision and Pattern Recognition — 38 citations into it
• Model Architectures — 9 citations into it

Whom Guang-Tong Zhou trusts overall

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

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

Papers from Guang-Tong Zhou's perspective

Ordered by how strongly the researchers Guang-Tong Zhou 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. Gradient-based learning applied to document recognition
6. ImageNet Large Scale Visual Recognition Challenge
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Going deeper with convolutions
9. ImageNet: A large-scale hierarchical image database
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• On the Class Imbalance Problem (2008) — 586 citations
• Structured Label Inference for Visual Understanding (2018) — 23 citations