Shengna Guo

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; #26,806 of 35,345 by trust here, #19,873 by citations
• Building and Construction — 33 citations into it; #369 of 2,909 by trust here, #477 by citations
• Transportation — 28 citations into it; #464 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 Shengna Guo trusts overall

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

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

Who trusts Shengna Guo overall

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

Papers from Shengna Guo's perspective

Ordered by how strongly the researchers Shengna Guo 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. Long Short-Term Memory
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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

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