Xin Yuan

Computer Vision and Pattern Recognition · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2015–2021

A researcher in the Science corpus: 5 papers; trusts 399 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 88 citations into it; #13,121 of 21,575 by trust here, #6,671 by citations
• Computational Mechanics — 49 citations into it; #2,141 of 10,001 by trust here, #7,173 by citations
• Artificial Intelligence — 42 citations into it
• Media Technology — 16 citations into it
• Model Architectures — 13 citations into it
• Generative Models — 12 citations into it
• Biomedical Engineering — 11 citations into it; #1,305 of 10,734 by trust here, #9,262 by citations
• Radiology, Nuclear Medicine and Imaging — 10 citations into it

Whom Xin Yuan trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. José M. Bioucas-Dias
4. Ashwin A. Wagadarikar
5. Yann LeCun
6. Diederik P. Kingma
7. Mário A. T. Figueiredo
8. Yoshua Bengio
9. E.J. Candes
10. Kaiming He

Who trusts Xin Yuan overall

1. Yuanhao Cai
2. Jing Lin
3. David J. Brady
4. Yang Liu
5. Aggelos K. Katsaggelos
6. Xiaopeng Li
7. Haoqian Wang
8. Xiaowan Hu
9. Yunchen Pu
10. J. Suo

Papers from Xin Yuan's perspective

Ordered by how strongly the researchers Xin Yuan 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Variational Autoencoder for Deep Learning of Images, Labels and Captions (2016) — 851 citations
• Snapshot Compressive Imaging: Theory, Algorithms, and Applications (2021) — 332 citations
• Rank Minimization for Snapshot Compressive Imaging (2018) — 426 citations
• Generalized alternating projection based total variation minimization for compressive sensing (2015) — 393 citations
• A Deep Generative Deconvolutional Image Model (2015) — 45 citations