Han Li

Artificial Intelligence · with Zhu · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 19 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it; #33,246 of 35,345 by trust here, #9,870 by citations
• Information Systems — 35 citations into it
• Computer Vision and Pattern Recognition — 22 citations into it; #20,922 of 21,575 by trust here, #7,340 by citations
• Recommender Systems — 17 citations into it
• Model Architectures — 11 citations into it
• Marketing — 10 citations into it
• Natural Language Processing — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Han Li trusts overall

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

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

Who trusts Han Li overall

1. Han Zhu

Papers from Han Li's perspective

Ordered by how strongly the researchers Han Li trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Deep Interest Network for Click-Through Rate Prediction (2017) — 2,280 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