Ruimin Ke

Building and Construction · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 86 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Building and Construction — 67 citations into it; #1,370 of 2,909 by trust here, #1,105 by citations
• Artificial Intelligence — 52 citations into it
• Control and Systems Engineering — 52 citations into it; #4,992 of 8,257 by trust here, #2,376 by citations
• Transportation — 38 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 — 15 citations into it

Whom Ruimin Ke trusts overall

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

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

Who trusts Ruimin Ke overall

1. Saeed Rahmani
2. David Alexander Tedjopurnomo
3. Zhiyong Cui
4. Osama Alkadi
5. Zulong Diao

Papers from Ruimin Ke's perspective

Ordered by how strongly the researchers Ruimin Ke 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. 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

• Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020) — 430 citations
• Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving (2019) — 419 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