Shuhao Cao

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 336 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it
• Model Architectures — 21 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Statistical and Nonlinear Physics — 15 citations into it; #1,920 of 10,127 by trust here, #8,564 by citations
• Computational Mechanics — 9 citations into it; #2,229 of 10,001 by trust here, #8,566 by citations
• Natural Language Processing — 7 citations into it
• Electrical and Electronic Engineering — 6 citations into it

Whom Shuhao Cao trusts overall

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

1. Sifan Wang
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Zong-Yi Li
5. Kaiming He
6. Ilya Sutskever
7. Alex Krizhevsky
8. Diederik P. Kingma
9. Xavier Glorot
10. Philippe G. Ciarlet

Who trusts Shuhao Cao overall

1. Zijie Li
2. Haixu Wu
3. Zhongkai Hao
4. Kazem Meidani
5. Huakun Luo
6. Amir Barati Farimani
7. Chengyang Ying
8. Haowen Wang
9. John Guibas
10. Zhengyi Wang

Papers from Shuhao Cao's perspective

Ordered by how strongly the researchers Shuhao Cao 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Choose a Transformer: Fourier or Galerkin (2021) — 450 citations