Youfang Lin

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 5 papers; trusts 152 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #26,807 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 37 citations into it
• Building and Construction — 33 citations into it; #370 of 2,909 by trust here, #477 by citations
• Electrical and Electronic Engineering — 28 citations into it
• Transportation — 28 citations into it; #465 of 2,062 by trust here, #292 by citations
• Model Architectures — 25 citations into it
• Graph Learning — 20 citations into it
• Signal Processing — 19 citations into it

Whom Youfang Lin trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Kaiming He
5. Yann LeCun
6. Alex Graves
7. Sepp Hochreiter
8. Ilya Sutskever
9. Alex Krizhevsky
10. Jimmy Ba

Who trusts Youfang Lin overall

1. Zezhi Shao
2. Shengna Guo
3. Saeed Rahmani
4. Zhao Zhang
5. Chao Song
6. Mengzhang Li
7. Xueyan Yin
8. Guangyin Jin
9. Huaiyu Wan
10. Jeongwhan Choi

Papers from Youfang Lin's perspective

Ordered by how strongly the researchers Youfang Lin 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Cross-Domain WiFi Sensing with Channel State Information: A Survey (2022) — 759 citations
• Learning Dynamics and Heterogeneity of Spatial-Temporal Graph Data for Traffic Forecasting (2021) — 718 citations
• Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting (2020) — 1,748 citations
• Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (2019) — 3,235 citations
• Deep Spatial–Temporal 3D Convolutional Neural Networks for Traffic Data Forecasting (2019) — 385 citations