Yiyang Zhao

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 3 papers; trusts 209 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 73 citations into it; #18,302 of 35,345 by trust here, #20,377 by citations
• Computer Vision and Pattern Recognition — 62 citations into it; #11,543 of 21,575 by trust here, #13,542 by citations
• Model Architectures — 33 citations into it
• Neural Architecture Search & AutoML — 18 citations into it; #145 of 283 by trust here, #195 by citations
• Optimization & Training — 17 citations into it
• Reinforcement Learning — 14 citations into it
• Management Science and Operations Research — 7 citations into it

Whom Yiyang Zhao trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Barret Zoph
4. Yoshua Bengio
5. Kaiming He
6. Frank Hutter
7. Quoc V. Le
8. Diederik P. Kingma
9. Richard S. Sutton
10. Yann LeCun

Who trusts Yiyang Zhao overall

1. Colin White
2. Yash Savani
3. Sam Nolen
4. Chepuri Shri Krishna
5. Vu Nguyen
6. Han Shi
7. Chen Wei

Papers from Yiyang Zhao's perspective

Ordered by how strongly the researchers Yiyang 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search (2019) — 100 citations
• Neural Architecture Search Using Deep Neural Networks and Monte Carlo Tree Search (2018) — 56 citations
• Superneurons: dynamic GPU memory management for training deep neural networks (2018) — 294 citations