Kun Gai

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 9 papers; trusts 85 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 106 citations into it; #35,177 of 35,345 by trust here, #8,283 by citations
• Information Systems — 69 citations into it
• Computer Vision and Pattern Recognition — 65 citations into it
• Model Architectures — 34 citations into it
• Recommender Systems — 28 citations into it
• Management Science and Operations Research — 24 citations into it
• Natural Language Processing — 13 citations into it
• Marketing — 11 citations into it

Whom Kun Gai trusts overall

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

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

Who trusts Kun Gai overall

1. Han Zhu
2. Guorui Zhou
3. Qi Pi
4. Pengye Zhang

Papers from Kun Gai's perspective

Ordered by how strongly the researchers Kun Gai 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. Learning Multiple Layers of Features from Tiny Images
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Going deeper with convolutions
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 Tree-based Deep Model for Recommender Systems (2018) — 346 citations
• Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction (2017) — 91 citations
• Optimized Cost per Click in Taobao Display Advertising (2017) — 171 citations