Fu-Yun Wang

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 3 papers; trusts 224 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 117 citations into it; #10,553 of 35,345 by trust here, #12,761 by citations
• Computer Vision and Pattern Recognition — 88 citations into it; #7,222 of 21,575 by trust here, #8,831 by citations
• Meta & Continual Learning — 49 citations into it; #331 of 1,041 by trust here, #443 by citations
• Representation & Self-Supervised Learning — 35 citations into it; #2,492 of 3,145 by trust here, #2,516 by citations
• Optimization & Training — 18 citations into it
• Radiology, Nuclear Medicine and Imaging — 18 citations into it; #2,021 of 3,265 by trust here, #2,257 by citations
• Efficiency & Compression — 10 citations into it
• Model Architectures — 8 citations into it

Whom Fu-Yun Wang trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Jia Deng
6. Diederik P. Kingma
7. Yann LeCun
8. Da-Wei Zhou
9. X. Zhang
10. Ross B. Girshick

Who trusts Fu-Yun Wang overall

1. Da-Wei Zhou
2. Han-Jia Ye
3. Liyuan Wang
4. De-chuan Zhan
5. Qiwen Wang
6. Dipam Goswami
7. Zhanxin Gao
8. Yizheng Gong
9. Jie M. Zhang
10. Hai-Long Sun

Papers from Fu-Yun Wang's perspective

Ordered by how strongly the researchers Fu-Yun Wang 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Forward Compatible Few-Shot Class-Incremental Learning (2022) — 288 citations
• FOSTER: Feature Boosting and Compression for Class-Incremental Learning (2022) — 428 citations
• PyCIL: a Python toolbox for class-incremental learning (2021) — 145 citations