Hao Wang

Artificial Intelligence · IEEE Transactions on Knowledge and Data Engineering · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 138 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #11,748 of 35,345 by trust here, #19,873 by citations
• Computer Vision and Pattern Recognition — 67 citations into it; #10,282 of 21,575 by trust here, #15,955 by citations
• Representation & Self-Supervised Learning — 25 citations into it; #2,066 of 3,145 by trust here, #2,863 by citations
• Meta & Continual Learning — 22 citations into it; #604 of 1,041 by trust here, #925 by citations
• Natural Language Processing — 14 citations into it
• Computational Mechanics — 13 citations into it
• Media Technology — 12 citations into it; #2,863 of 3,621 by trust here, #3,522 by citations
• Reinforcement Learning — 7 citations into it

Whom Hao Wang trusts overall

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

1. Feiping Nie
2. Geoffrey E. Hinton
3. Zhiyuan Chen
4. Yoshua Bengio
5. Alex Krizhevsky
6. Sebastian Thrun
7. Chih-Jen Lin
8. Diederik P. Kingma
9. Yann LeCun
10. Kaiming He

Who trusts Hao Wang overall

1. Zixuan Ke
2. Bing Liu
3. Qi Qin
4. Erlin Pan
5. Hu Xu
6. Zhao Kang
7. Wenpeng Hu
8. Lei Shu
9. Xingchang Huang
10. Wen-Rui Hu

Papers from Hao Wang's perspective

Ordered by how strongly the researchers Hao Wang 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Continual Learning with Knowledge Transfer for Sentiment Classification (2021) — 36 citations
• GMC: Graph-Based Multi-View Clustering (2020) — 805 citations
• Forward and Backward Knowledge Transfer for Sentiment Classification (2019) — 18 citations
• A study of graph-based system for multi-view clustering (2019) — 339 citations
• Multi-view clustering: A survey (2018) — 436 citations