Zheng Qin

Artificial Intelligence · with Chen · 2016–2019

A researcher in the Science corpus: 6 papers; trusts 165 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it; #15,816 of 35,345 by trust here, #11,750 by citations
• Information Systems — 105 citations into it; #3,050 of 8,771 by trust here, #1,815 by citations
• Computer Vision and Pattern Recognition — 62 citations into it
• Natural Language Processing — 32 citations into it
• Recommender Systems — 28 citations into it
• Model Architectures — 22 citations into it
• Management Science and Operations Research — 15 citations into it
• Marketing — 12 citations into it

Whom Zheng Qin trusts overall

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

1. Julian McAuley
2. Geoffrey E. Hinton
3. Ruslan Salakhutdinov
4. Steffen Rendle
5. Alex Krizhevsky
6. Yoshua Bengio
7. Diederik P. Kingma
8. Yongfeng Zhang
9. Ilya Sutskever
10. Yann LeCun

Who trusts Zheng Qin overall

1. Xu Chen
2. Yongfeng Zhang
3. Jiaxi Tang
4. Konstantin Bauman
5. Roberto Confalonieri
6. Ramesh Baral
7. Hanxiong Chen

Papers from Zheng Qin's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Personalized Fashion Recommendation with Visual Explanations based on Multimodal Attention Network: Towards Visually Explainable Recommendation (2019) — 358 citations
• Dynamic Explainable Recommendation Based on Neural Attentive Models (2019) — 142 citations
• Adversarial Distillation for Efficient Recommendation with External Knowledge (2018) — 62 citations
• Sequential Recommendation with User Memory Networks (2018) — 544 citations
• Visually Explainable Recommendation (2018) — 54 citations
• Learning to Rank Features for Recommendation over Multiple Categories (2016) — 162 citations