Xiaoqiang Zhu

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 63 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #34,258 of 35,345 by trust here, #8,706 by citations
• Computer Vision and Pattern Recognition — 58 citations into it; #20,399 of 21,575 by trust here, #6,089 by citations
• Information Systems — 52 citations into it
• Model Architectures — 31 citations into it
• Management Science and Operations Research — 19 citations into it
• Recommender Systems — 18 citations into it
• Optimization & Training — 10 citations into it
• Natural Language Processing — 9 citations into it

Whom Xiaoqiang Zhu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Steffen Rendle
6. Tom Fawcett
7. Alex Krizhevsky
8. Michael I. Jordan
9. Dimitri P. Bertsekas
10. Tong Zhang

Who trusts Xiaoqiang Zhu overall

1. Qi Pi
2. Han Zhu
3. Guorui Zhou

Papers from Xiaoqiang Zhu's perspective

Ordered by how strongly the researchers Xiaoqiang Zhu 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. Going deeper with convolutions
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Deep Interest Network for Click-Through Rate Prediction (2017) — 2,280 citations
• Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net (2017) — 129 citations
• Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate (2018) — 540 citations
• Deep Interest Evolution Network for Click-Through Rate Prediction (2018) — 1,336 citations
• Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction (2020) — 457 citations
• Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction (2019) — 332 citations
• Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction (2017) — 91 citations