Fei Zhu

Artificial Intelligence · with Liu · 2021

A researcher in the Science corpus: 2 papers; trusts 206 researchers and is trusted by 22 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #14,964 of 35,345 by trust here, #14,181 by citations
• Computer Vision and Pattern Recognition — 69 citations into it; #9,659 of 21,575 by trust here, #9,722 by citations
• Meta & Continual Learning — 36 citations into it; #231 of 1,041 by trust here, #391 by citations
• Representation & Self-Supervised Learning — 30 citations into it; #1,633 of 3,145 by trust here, #1,781 by citations
• Optimization & Training — 11 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Generative Models — 6 citations into it
• Model Architectures — 6 citations into it

Whom Fei Zhu trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Diederik P. Kingma
5. Yann LeCun
6. Yoshua Bengio
7. Ilya Sutskever
8. Jimmy Ba
9. Ross B. Girshick
10. Oriol Vinyals

Who trusts Fei Zhu overall

1. Gyuhak Kim
2. Da-Wei Zhou
3. Liyuan Wang
4. Yiduo Guo
5. Simone Magistri
6. Grégoire Petit
7. Jaya Kalla
8. Grzegorz Rype's'c
9. Changnan Xiao
10. Qiwen Wang

Papers from Fei Zhu's perspective

Ordered by how strongly the researchers Fei Zhu 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. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Prototype Augmentation and Self-Supervision for Incremental Learning (2021) — 529 citations
• Class-Incremental Learning via Dual Augmentation (2021) — 244 citations