Huaiyu Wan

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 70 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 55 citations into it; #26,808 of 35,345 by trust here, #19,873 by citations
• Building and Construction — 33 citations into it; #371 of 2,909 by trust here, #477 by citations
• Transportation — 28 citations into it; #466 of 2,062 by trust here, #292 by citations
• Model Architectures — 25 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it
• Graph Learning — 20 citations into it
• Management Science and Operations Research — 10 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Huaiyu Wan 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. Alex Graves
6. Sepp Hochreiter
7. Yann LeCun
8. Ilya Sutskever
9. Jimmy Ba
10. Alex Krizhevsky

Who trusts Huaiyu Wan overall

1. Zezhi Shao
2. Shengna Guo
3. Zhao Zhang
4. Youfang Lin

Papers from Huaiyu Wan's perspective

Ordered by how strongly the researchers Huaiyu Wan 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. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• 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