Hongyun Cai
Artificial Intelligence · 2016–2017
A researcher in the Science corpus: 3 papers; trusts 126 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 127 citations into it; #17,364 of 35,345 by trust here, #11,750 by citations
- Graph Learning — 67 citations into it; #411 of 693 by trust here, #330 by citations
- Computer Vision and Pattern Recognition — 58 citations into it
- Representation & Self-Supervised Learning — 53 citations into it
- Statistical and Nonlinear Physics — 53 citations into it; #4,351 of 10,127 by trust here, #1,906 by citations
- Molecular Biology — 12 citations into it
- Natural Language Processing — 12 citations into it
- Management Science and Operations Research — 9 citations into it
Whom Hongyun Cai trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Yoshua Bengio
- Yann LeCun
- Diederik P. Kingma
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Tomas Mikolov
- Shaosheng Cao
- Max Welling
Who trusts Hongyun Cai overall
- Sergey Ivanov
- Zemin Liu
- Liang Yao
- Dingqi Yang
- Fernando Gama
- Vincent Wenchen Zheng
- Hanqing Zeng
- Aleksandar Bojchevski
- Feng Xia
- Rong Zhu
Papers from Hongyun Cai's perspective
Ordered by how strongly the researchers Hongyun Cai trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017) — 1,965 citations
- Learning Community Embedding with Community Detection and Node Embedding on Graphs (2017) — 395 citations
- From Node Embedding To Community Embedding (2016) — 34 citations