Kun Zhang

Artificial Intelligence · AAAI Conference on Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 87 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 169 citations into it
• Information Systems — 143 citations into it
• Computer Vision and Pattern Recognition — 68 citations into it
• Management Science and Operations Research — 36 citations into it
• Recommender Systems — 35 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Natural Language Processing — 23 citations into it; #3,195 of 5,688 by trust here, #4,793 by citations
• Graph Learning — 18 citations into it

Whom Kun Zhang trusts overall

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

1. Xiangnan He
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Steffen Rendle
6. Alex Krizhevsky
7. Ilya Sutskever
8. Yann LeCun
9. Kaiming He
10. Xiang Wang

Who trusts Kun Zhang overall

1. Bing Liu
2. Zixuan Ke

Papers from Kun Zhang's perspective

Ordered by how strongly the researchers Kun Zhang 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. 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. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Neural News Recommendation with Long- and Short-term User Representations (2019) — 326 citations
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021) — 445 citations
• Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach (2020) — 654 citations
• Sentiment Classification by Leveraging the Shared Knowledge from a Sequence of Domains (2019) — 44 citations