Zikun Hu

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 28 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #25,041 of 35,345 by trust here, #23,731 by citations
• Information Systems — 22 citations into it; #5,638 of 8,771 by trust here, #5,303 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Natural Language Processing — 13 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Management Science and Operations Research — 8 citations into it
• Recommender Systems — 6 citations into it
• Model Architectures — 5 citations into it

Whom Zikun Hu trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Ilya Sutskever
4. Diederik P. Kingma
5. Tomas Mikolov
6. Yoshua Bengio
7. Sepp Hochreiter
8. Yann LeCun
9. Yoon Kim
10. Jimmy Ba

Who trusts Zikun Hu overall

1. Chengwei Qin
2. Ilias Chalkidis
3. Shafiq R. Joty
4. Chaojun Xiao
5. Haoxiang Zhong
6. Yaqing Wang
7. Yisheng Song
8. Zixuan Ke
9. Xiang Wang
10. Ion Androutsopoulos

Papers from Zikun Hu's perspective

Ordered by how strongly the researchers Zikun Hu 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences (2019) — 695 citations
• Few-Shot Charge Prediction with Discriminative Legal Attributes (2018) — 250 citations