Junru Shao

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 141 researchers and is trusted by 39 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #5,082 of 35,345 by trust here, #10,566 by citations
• Computer Vision and Pattern Recognition — 29 citations into it; #4,347 of 21,575 by trust here, #7,421 by citations
• Natural Language Processing — 25 citations into it; #1,150 of 5,688 by trust here, #2,307 by citations
• Optimization & Training — 16 citations into it
• Model Architectures — 13 citations into it
• Computational Mechanics — 12 citations into it
• Scaling & Learning Theory — 11 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Junru Shao trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Yann LeCun
5. Ilya Sutskever
6. Karen Simonyan
7. Diederik P. Kingma
8. Christopher D. Manning
9. Kyunghyun Cho
10. Kaiming He

Who trusts Junru Shao overall

1. Xinya Du
2. Vishwajeet Kumar
3. Linfeng Song
4. Claire Cardie
5. Thomas Scialom
6. Nan Duan
7. Duyu Tang
8. Tong Wang
9. Shiyue Zhang
10. Patrick Lewis

Papers from Junru Shao's perspective

Ordered by how strongly the researchers Junru Shao 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Learning to Ask: Neural Question Generation for Reading Comprehension (2017) — 741 citations
• Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex (2018) — 14 citations