Liu Liu

Artificial Intelligence · with Tao · 2017–2022

A researcher in the Science corpus: 9 papers; trusts 886 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 280 citations into it; #18,025 of 35,345 by trust here, #16,771 by citations
• Computer Vision and Pattern Recognition — 220 citations into it; #15,840 of 21,575 by trust here, #15,955 by citations
• Meta & Continual Learning — 125 citations into it; #550 of 1,041 by trust here, #808 by citations
• Representation & Self-Supervised Learning — 91 citations into it
• Optimization & Training — 50 citations into it
• Model Architectures — 45 citations into it
• Radiology, Nuclear Medicine and Imaging — 25 citations into it
• Efficiency & Compression — 21 citations into it

Whom Liu Liu trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Yoshua Bengio
5. Yann LeCun
6. Diederik P. Kingma
7. Ilya Sutskever
8. Marc'Aurelio Ranzato
9. Ross B. Girshick
10. Arslan Chaudhry

Who trusts Liu Liu overall

1. Zhen Wang
2. Liyuan Wang
3. Liangzu Peng
4. Dacheng Tao
5. Yajing Kong
6. Yiqun Duan
7. Jixiang Guo
8. Tong Wei
9. Xingxing Zhang
10. Prashant Bhat

Papers from Liu Liu's perspective

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

Their papers

• Label Propagation via Teaching-to-Learn and Learning-to-Teach (2017) — 136 citations
• Balancing Stability and Plasticity through Advanced Null Space in Continual Learning (2022) — 53 citations
• Continual Learning with Lifelong Vision Transformer (2022) — 99 citations
• Online Continual Learning with Contrastive Vision Transformer (2022) — 45 citations
• SIN: Semantic Inference Network for Few-Shot Streaming Label Learning (2022) — 10 citations
• Continual Learning through Retrieval and Imagination (2022) — 48 citations
• Multi-label Few-shot Learning with Semantic Inference (Student Abstract) (2021) — 4 citations
• Adaptive Curriculum Learning (2021) — 66 citations
• Deep Streaming Label Learning (2020) — 42 citations