Yaguang Li

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it
• Transportation — 15 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Graph Learning — 9 citations into it; #311 of 693 by trust here, #210 by citations
• Model Architectures — 7 citations into it
• Optimization & Training — 6 citations into it
• Natural Language Processing — 5 citations into it

Whom Yaguang Li trusts overall

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

1. Ilya Sutskever
2. Yoshua Bengio
3. Oriol Vinyals
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Peter J. Brockwell
7. Alex Krizhevsky
8. Michaël Defferrard
9. Yann LeCun
10. Yu Zheng

Who trusts Yaguang Li overall

1. Zhiyong Cui
2. Zezhi Shao
3. Senzhang Wang
4. Lei Bai
5. Weiwei Jiang
6. Saeed Rahmani
7. Mengzhang Li
8. Juan Miguel Lopez Alcaraz
9. David Alexander Tedjopurnomo
10. Qi Zhang

Papers from Yaguang Li's perspective

Ordered by how strongly the researchers Yaguang Li trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting (2019) — 878 citations
• Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (2017) — 4,347 citations
• Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting (2017) — 395 citations