Rui Zhao

Artificial Intelligence · with Bieber · 2018

A researcher in the Science corpus: 2 papers; trusts 54 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #10,904 of 35,345 by trust here, #5,002 by citations
• Natural Language Processing — 54 citations into it; #2,299 of 5,688 by trust here, #1,205 by citations
• Computer Vision and Pattern Recognition — 33 citations into it; #9,640 of 21,575 by trust here, #3,848 by citations
• Information Systems — 15 citations into it
• Model Architectures — 13 citations into it; #1,171 of 2,278 by trust here, #502 by citations
• Software — 8 citations into it
• Code Generation & Program Synthesis — 5 citations into it

Whom Rui Zhao trusts overall

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

1. Yoshua Bengio
2. Miltiadis Allamanis
3. Ilya Sutskever
4. Oriol Vinyals
5. Sepp Hochreiter
6. Geoffrey E. Hinton
7. Dzmitry Bahdanau
8. Premkumar T. Devanbu
9. J. Urgen Schmidhuber
10. Diederik P. Kingma

Who trusts Rui Zhao overall

1. Eric Wallace
2. Jie Lei
3. Hsin-Yuan Huang
4. Nelson F. Liu
5. Uri Alon
6. Michihiro Yasunaga

Papers from Rui Zhao's perspective

Ordered by how strongly the researchers Rui Zhao 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension (2018) — 1,133 citations
• Neural Networks for Modeling Source Code Edits (2018) — 12 citations