Yaoyao Liu

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 9 papers; trusts 1,274 researchers and is trusted by 179 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 316 citations into it; #3,105 of 35,345 by trust here, #3,400 by citations
• Computer Vision and Pattern Recognition — 278 citations into it; #2,703 of 21,575 by trust here, #2,814 by citations
• Meta & Continual Learning — 142 citations into it; #66 of 1,041 by trust here, #81 by citations
• Representation & Self-Supervised Learning — 123 citations into it; #1,447 of 3,145 by trust here, #1,277 by citations
• Optimization & Training — 66 citations into it
• Radiology, Nuclear Medicine and Imaging — 51 citations into it; #449 of 3,265 by trust here, #869 by citations
• Model Architectures — 45 citations into it
• Reinforcement Learning — 34 citations into it

Whom Yaoyao Liu 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. Diederik P. Kingma
6. Ross B. Girshick
7. Yann LeCun
8. Ilya Sutskever
9. Max Welling
10. Radford M. Neal

Who trusts Yaoyao Liu overall

1. Gyuhak Kim
2. Da-Wei Zhou
3. Huiping Zhuang
4. Bernt Schiele
5. Liyuan Wang
6. Guangyuan Shi
7. Qianru Sun
8. Han-Jia Ye
9. Chi Zhang
10. Jiangpeng He

Papers from Yaoyao Liu's perspective

Ordered by how strongly the researchers Yaoyao 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. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• Meta-Transfer Learning for Few-Shot Learning (2018) — 1,232 citations
• Mnemonics Training: Multi-Class Incremental Learning Without Forgetting (2020) — 413 citations
• Adaptive Aggregation Networks for Class-Incremental Learning (2020) — 284 citations
• Learning to Self-Train for Semi-Supervised Few-Shot Classification (2019) — 301 citations
• Meta-Transfer Learning Through Hard Tasks (2019) — 128 citations
• Continual Detection Transformer for Incremental Object Detection (2023) — 122 citations
• RMM: Reinforced Memory Management for Class-Incremental Learning (2023) — 130 citations
• Meta-Aggregating Networks for Class-Incremental Learning (2020) — 6 citations
• An Ensemble of Epoch-Wise Empirical Bayes for Few-Shot Learning (2019) — 136 citations