Miao Zhao

Artificial Intelligence · The Web Conference · 2017–2019

A researcher in the Science corpus: 7 papers; trusts 81 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 169 citations into it; #13,124 of 35,345 by trust here, #7,112 by citations
• Information Systems — 100 citations into it; #3,142 of 8,771 by trust here, #1,220 by citations
• Computer Vision and Pattern Recognition — 67 citations into it
• Representation & Self-Supervised Learning — 59 citations into it
• Graph Learning — 55 citations into it
• Recommender Systems — 45 citations into it; #142 of 233 by trust here, #95 by citations
• Knowledge Graphs & Representation — 32 citations into it; #181 of 226 by trust here, #102 by citations
• Natural Language Processing — 27 citations into it

Whom Miao Zhao trusts overall

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

1. Geoffrey E. Hinton
2. Steffen Rendle
3. Hongwei Wang
4. Yoshua Bengio
5. Tomas Mikolov
6. Ilya Sutskever
7. Diederik P. Kingma
8. Alex Krizhevsky
9. Yann LeCun
10. Ruslan Salakhutdinov

Who trusts Miao Zhao overall

1. Hongwei Wang
2. Xiang Wang
3. Shiwen Wu
4. Qingyu Guo
5. Fuzheng Zhang
6. Xiaohan Zou
7. Xing Xie
8. Chen Gao
9. Fei Sun
10. Xinyu Fu

Papers from Miao Zhao's perspective

Ordered by how strongly the researchers Miao Zhao 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. 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. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• GraphGAN: Graph Representation Learning with Generative Adversarial Nets (2017) — 698 citations
• Knowledge Graph Convolutional Networks for Recommender Systems (2019) — 1,150 citations
• Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation (2019) — 536 citations
• Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems (2019) — 660 citations
• RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018) — 1,246 citations
• Ripple Network: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018) — 34 citations
• Joint Topic-Semantic-aware Social Recommendation for Online Voting (2017) — 32 citations