Lu Liu

Artificial Intelligence · with Jiang · 2020–2022

A researcher in the Science corpus: 3 papers; trusts 132 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it; #26,487 of 35,345 by trust here, #27,551 by citations
• Computer Vision and Pattern Recognition — 55 citations into it; #16,212 of 21,575 by trust here, #18,782 by citations
• Representation & Self-Supervised Learning — 31 citations into it
• Optimization & Training — 11 citations into it
• Information Systems — 10 citations into it
• Model Architectures — 8 citations into it

Whom Lu Liu trusts overall

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

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

Who trusts Lu Liu overall

1. Wei-Hong Li
2. Yue Tan
3. Xialei Liu
4. Eleni Triantafillou
5. Yisheng Song
6. Guodong Long
7. Hakan Bilen
8. Hugo Larochelle

Papers from Lu Liu's perspective

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

• A Universal Representation Transformer Layer for Few-Shot Image Classification (2020) — 144 citations
• Federated Learning from Pre-Trained Models: A Contrastive Learning Approach (2022) — 296 citations
• FedProto: Federated Prototype Learning across Heterogeneous Clients (2021) — 940 citations