Zonghan Wu

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 48 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 131 citations into it; #12,149 of 35,345 by trust here, #3,539 by citations
• Graph Learning — 86 citations into it; #605 of 693 by trust here, #106 by citations
• Computer Vision and Pattern Recognition — 73 citations into it; #18,581 of 21,575 by trust here, #3,191 by citations
• Statistical and Nonlinear Physics — 44 citations into it
• Model Architectures — 23 citations into it

Whom Zonghan Wu trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Oriol Vinyals
5. Yann LeCun
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Kaiming He
9. Christian Szegedy
10. Alex Graves

Who trusts Zonghan Wu overall

1. Zezhi Shao
2. Shirui Pan
3. Weiwei Jiang
4. Yixin Liu
5. Zhao Zhang
6. Shengna Guo
7. Chuanpan Zheng
8. Ming Jin
9. Shiyong Lan
10. Weiqiu Chen

Papers from Zonghan Wu's perspective

Ordered by how strongly the researchers Zonghan Wu 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• A Comprehensive Survey on Graph Neural Networks (2019) — 11,551 citations
• Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks (2020) — 2,163 citations
• Graph WaveNet for Deep Spatial-Temporal Graph Modeling (2019) — 3,288 citations