Fengwei Zhou

Computer Vision and Pattern Recognition · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 223 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 37 citations into it; #4,715 of 21,575 by trust here, #4,372 by citations
• Artificial Intelligence — 36 citations into it; #6,418 of 35,345 by trust here, #5,924 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Reinforcement Learning — 13 citations into it
• Model Architectures — 8 citations into it
• Optimization & Training — 8 citations into it

Whom Fengwei Zhou trusts overall

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

1. Ross B. Girshick
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Kaiming He
6. Yoshua Bengio
7. Ruslan Salakhutdinov
8. J. Urgen Schmidhuber
9. Gregory R. Koch
10. Ilya Sutskever

Who trusts Fengwei Zhou overall

1. Eli Schwartz
2. Han-Jia Ye
3. Andrei A. Rusu
4. Fei Chen
5. Yogesh Kumar Balaji
6. Antreas Antoniou
7. Limeng Qiao
8. Jack W. Rae
9. Leonid Karlinsky
10. Luisa M. Zintgraf

Papers from Fengwei Zhou's perspective

Ordered by how strongly the researchers Fengwei 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. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• Meta-SGD: Learning to Learn Quickly for Few Shot Learning (2017) — 1,259 citations
• Deep Meta-Learning: Learning to Learn in the Concept Space (2018) — 134 citations