Yu Liu

Artificial Intelligence · with Tuytelaars · 2020–2022

A researcher in the Science corpus: 2 papers; trusts 273 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 76 citations into it; #26,002 of 35,345 by trust here, #18,586 by citations
• Computer Vision and Pattern Recognition — 75 citations into it; #15,954 of 21,575 by trust here, #12,689 by citations
• Meta & Continual Learning — 35 citations into it; #816 of 1,041 by trust here, #692 by citations
• Representation & Self-Supervised Learning — 30 citations into it
• Model Architectures — 10 citations into it
• Optimization & Training — 7 citations into it

Whom Yu Liu trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Ross B. Girshick
5. Yoshua Bengio
6. Yann LeCun
7. Diederik P. Kingma
8. Ilya Sutskever
9. Zhizhong Li
10. Karen Simonyan

Who trusts Yu Liu overall

1. Gyuhak Kim
2. Subhankar Roy
3. Changnan Xiao
4. Fei Zhu
5. Yajing Kong
6. Mingfu Liang
7. Shipeng Wang
8. Grégoire Petit
9. Chang-Bin Zhang
10. Xiaomeng Xin

Papers from Yu Liu's perspective

Ordered by how strongly the researchers Yu Liu 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

• More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning (2020) — 115 citations
• Residual Tuning: Toward Novel Category Discovery Without Labels (2022) — 36 citations