Shengyu Zhao

Computer Vision and Pattern Recognition · 2019–2021

A researcher in the Science corpus: 4 papers; trusts 443 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 202 citations into it; #12,443 of 21,575 by trust here, #6,485 by citations
• Generative Models — 73 citations into it; #1,485 of 1,956 by trust here, #846 by citations
• Artificial Intelligence — 64 citations into it
• Computational Mechanics — 27 citations into it
• Optimization & Training — 17 citations into it
• Efficiency & Compression — 16 citations into it
• Geology — 16 citations into it
• Model Architectures — 12 citations into it

Whom Shengyu Zhao trusts overall

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

1. Alex Krizhevsky
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Ilya Sutskever
7. Yann LeCun
8. Ross B. Girshick
9. Karen Simonyan
10. Jimmy Ba

Who trusts Shengyu Zhao overall

1. Axel Sauer
2. Shiyu Chang
3. Tony C. W. Mok
4. Jongheon Jeong
5. Zhisheng Xiao
6. Gaurav Parmar
7. Sam Bond-Taylor
8. Alexia Jolicoeur-Martineau
9. Bingchen Liu
10. Yijun Li

Papers from Shengyu Zhao's perspective

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

Their papers

• Differentiable Augmentation for Data-Efficient GAN Training (2020) — 694 citations
• Large Scale Image Completion via Co-Modulated Generative Adversarial Networks (2021) — 359 citations
• Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution (2020) — 863 citations
• Recursive Cascaded Networks for Unsupervised Medical Image Registration (2019) — 300 citations