Shaosheng Cao

Artificial Intelligence · 2015–2016

A researcher in the Science corpus: 2 papers; trusts 58 researchers and is trusted by 66 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #3,068 of 35,345 by trust here, #5,749 by citations
• Representation & Self-Supervised Learning — 31 citations into it; #643 of 3,145 by trust here, #1,234 by citations
• Natural Language Processing — 18 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it; #5,627 of 21,575 by trust here, #7,499 by citations
• Graph Learning — 9 citations into it; #78 of 693 by trust here, #146 by citations
• Statistical and Nonlinear Physics — 9 citations into it; #1,349 of 10,127 by trust here, #1,638 by citations

Whom Shaosheng Cao trusts overall

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

1. Lei Tang
2. Geoffrey E. Hinton
3. John A. Bullinaria
4. Omer Levy
5. Tomas Mikolov
6. Yoshua Bengio
7. Ilya Sutskever
8. Richard Socher
9. Jeffrey Pennington
10. Huan Liu

Who trusts Shaosheng Cao overall

1. Bryan Perozzi
2. Daokun Zhang
3. William L. Hamilton
4. Shirui Pan
5. Aleksandar Bojchevski
6. Peng Cui
7. Liyu Gong
8. Hongyun Cai
9. Benedek Rozemberczki
10. Yassine Ouali

Papers from Shaosheng Cao's perspective

Ordered by how strongly the researchers Shaosheng Cao 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. 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Deep Neural Networks for Learning Graph Representations (2016) — 1,030 citations
• GraRep: Learning Graph Representations with Global Structural Information (2015) — 1,738 citations