Zhiyong Cui

Building and Construction · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 80 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Building and Construction — 63 citations into it; #1,369 of 2,909 by trust here, #1,105 by citations
• Artificial Intelligence — 48 citations into it
• Control and Systems Engineering — 42 citations into it; #4,996 of 8,257 by trust here, #2,447 by citations
• Transportation — 35 citations into it
• Signal Processing — 24 citations into it
• Model Architectures — 22 citations into it
• Graph Learning — 17 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it

Whom Zhiyong Cui trusts overall

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

1. Alex Graves
2. J. Urgen Schmidhuber
3. Sepp Hochreiter
4. Geoffrey E. Hinton
5. Yoshua Bengio
6. Diederik P. Kingma
7. Kyunghyun Cho
8. Yanjie Duan
9. Yann LeCun
10. Ilya Sutskever

Who trusts Zhiyong Cui overall

1. Saeed Rahmani
2. David Alexander Tedjopurnomo
3. Senzhang Wang
4. Osama Alkadi
5. Asiye Baghbani
6. B. Khemani
7. Zhifeng Bao
8. Zulong Diao
9. Fuxian Li
10. Nizar Bouguila

Papers from Zhiyong Cui's perspective

Ordered by how strongly the researchers Zhiyong Cui 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020) — 430 citations
• Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction (2018) — 464 citations
• Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018) — 905 citations
• High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018) — 82 citations