Xiuwen Yi

Artificial Intelligence · 2015–2018

A researcher in the Science corpus: 6 papers; trusts 21 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #26,554 of 35,345 by trust here, #18,186 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Transportation — 17 citations into it; #829 of 2,062 by trust here, #498 by citations
• Model Architectures — 16 citations into it
• Building and Construction — 14 citations into it; #1,104 of 2,909 by trust here, #945 by citations
• Optimization & Training — 13 citations into it
• Management Science and Operations Research — 11 citations into it
• Signal Processing — 10 citations into it

Whom Xiuwen Yi trusts overall

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

1. Yu Zheng
2. Junbo Zhang
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Jimmy Ba
6. Alex Krizhevsky
7. Sepp Hochreiter
8. Nitish Srivastava
9. Ilya Sutskever
10. Yann LeCun

Who trusts Xiuwen Yi overall

1. Haitao Yuan
2. Junbo Zhang
3. Wei Cao
4. Yu Zheng
5. Guoliang Li
6. Senzhang Wang
7. Shengdong Du
8. Tianrui Li
9. Jian Li
10. Wenxiu Ding

Papers from Xiuwen Yi's perspective

Ordered by how strongly the researchers Xiuwen Yi 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. ImageNet Large Scale Visual Recognition Challenge
9. ImageNet: A large-scale hierarchical image database
10. Dropout: a simple way to prevent neural networks from overfitting

Their papers

• GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction (2018) — 527 citations
• Deep Distributed Fusion Network for Air Quality Prediction (2018) — 328 citations
• Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks (2017) — 492 citations
• ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data (2016) — 214 citations
• DNN-based prediction model for spatio-temporal data (2016) — 743 citations
• Forecasting Fine-Grained Air Quality Based on Big Data (2015) — 519 citations