Jianxun Lian

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 4 papers; trusts 18 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it; #34,233 of 35,345 by trust here, #10,942 by citations
• Information Systems — 75 citations into it; #8,361 of 8,771 by trust here, #1,463 by citations
• Computer Vision and Pattern Recognition — 48 citations into it
• Recommender Systems — 31 citations into it
• Management Science and Operations Research — 18 citations into it
• Marketing — 10 citations into it
• Information Retrieval & Search — 9 citations into it

Whom Jianxun Lian trusts overall

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

1. Diederik P. Kingma
2. Yehuda Koren
3. Geoffrey E. Hinton
4. Steffen Rendle
5. Yoshua Bengio
6. Ruslan Salakhutdinov
7. Alex Krizhevsky
8. Jimmy Ba
9. Kaiming He
10. Weinan Zhang

Who trusts Jianxun Lian overall

1. Aminu Da’u
2. Tongwen Huang
3. Xiang Wang
4. Shuai Zhang
5. Xiangnan He
6. Weiping Song
7. Weijie Zhao
8. Naomie Binti Salim
9. Maxim Naumov
10. Le Wu

Papers from Jianxun Lian's perspective

Ordered by how strongly the researchers Jianxun Lian 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Content-Based Collaborative Filtering for News Topic Recommendation (2015) — 156 citations
• Self-supervised Graph Learning for Recommendation (2020) — 1,757 citations
• MIND: A Large-scale Dataset for News Recommendation (2020) — 670 citations
• xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems (2018) — 1,240 citations