Yaozong Gao

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 60 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 67 citations into it; #11,837 of 21,575 by trust here, #10,677 by citations
• Artificial Intelligence — 45 citations into it
• Model Architectures — 15 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Computational Mechanics — 8 citations into it; #9,176 of 10,001 by trust here, #9,276 by citations
• Modeling and Simulation — 5 citations into it

Whom Yaozong Gao trusts overall

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

1. Yoshua Bengio
2. David G. Lowe
3. Geoffrey E. Hinton
4. Pascal Vincent
5. Navneet Dalal
6. Hugo Larochelle
7. Yann LeCun
8. Kaiming He
9. Alex Krizhevsky
10. Honglak Lee

Who trusts Yaozong Gao overall

1. Arvid Lundervold
2. Yu-An Chung
3. Dinggang Shen
4. Hyunseok Seo

Papers from Yaozong Gao's perspective

Ordered by how strongly the researchers Yaozong Gao 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching (2015) — 238 citations
• Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia (2020) — 328 citations
• Lung Infection Quantification of COVID-19 in CT Images with Deep Learning (2020) — 586 citations
• The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge (2019) — 625 citations
• 7T-guided super-resolution of 3T MRI (2017) — 57 citations