Dekang Qi

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 83 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #11,041 of 35,345 by trust here, #9,465 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Model Architectures — 21 citations into it
• Transportation — 19 citations into it; #828 of 2,062 by trust here, #462 by citations
• Optimization & Training — 18 citations into it
• Signal Processing — 13 citations into it
• Building and Construction — 11 citations into it; #157 of 2,909 by trust here, #255 by citations
• Natural Language Processing — 10 citations into it

Whom Dekang Qi trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Yann LeCun
4. Diederik P. Kingma
5. Sergey Ioffe
6. Alex Krizhevsky
7. Ilya Sutskever
8. Yu Zheng
9. Christian Szegedy
10. Yoshua Bengio

Who trusts Dekang Qi overall

1. Junbo Zhang
2. Senzhang Wang
3. Yunbo Wang
4. Xiuwen Yi
5. Yu Zheng
6. Saeed Rahmani
7. Shengna Guo
8. Jiannong Cao
9. Haitao Yuan
10. Weiwei Jiang

Papers from Dekang Qi's perspective

Ordered by how strongly the researchers Dekang Qi 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. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet Large Scale Visual Recognition Challenge
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning (2020) — 313 citations
• Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks (2017) — 492 citations
• Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction (2016) — 2,444 citations
• DNN-based prediction model for spatio-temporal data (2016) — 743 citations