Weiwei Jiang

Management Science and Operations Research · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 306 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Management Science and Operations Research — 72 citations into it
• Artificial Intelligence — 63 citations into it
• Time-Series & Forecasting — 44 citations into it
• Building and Construction — 31 citations into it
• Signal Processing — 28 citations into it
• Electrical and Electronic Engineering — 27 citations into it
• Economics and Econometrics — 23 citations into it
• Transportation — 23 citations into it

Whom Weiwei Jiang trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Matthew D. Zeiler
5. Omer Berat Sezer
6. Tomas Mikolov
7. Yann LeCun
8. Alex Krizhevsky
9. Kyunghyun Cho
10. Dzmitry Bahdanau

Who trusts Weiwei Jiang overall

1. Guangyin Jin
2. Saeed Rahmani
3. Yuxuan Liang

Papers from Weiwei Jiang's perspective

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

Their papers

• Graph Neural Network for Traffic Forecasting: A Survey (2021) — 1,265 citations
• Applications of deep learning in stock market prediction: recent progress (2020) — 633 citations