Yushi Chen

Media Technology · 2014–2021

A researcher in the Science corpus: 7 papers; trusts 236 researchers and is trusted by 81 researchers here, as inferred from the citation graph.

Subfields

• Media Technology — 143 citations into it; #138 of 3,621 by trust here, #108 by citations
• Computer Vision and Pattern Recognition — 119 citations into it; #3,894 of 21,575 by trust here, #3,280 by citations
• Atmospheric Science — 85 citations into it; #97 of 4,804 by trust here, #151 by citations
• Artificial Intelligence — 81 citations into it
• Analytical Chemistry — 32 citations into it; #840 of 1,254 by trust here, #515 by citations
• Ecology — 29 citations into it
• Model Architectures — 28 citations into it
• Biomedical Engineering — 18 citations into it

Whom Yushi Chen trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Krizhevsky
4. Yann LeCun
5. Ilya Sutskever
6. Ruslan Salakhutdinov
7. Diederik P. Kingma
8. Jon Atli Benediktsson
9. Chih-Jen Lin
10. Kaiming He

Who trusts Yushi Chen overall

1. Mercedes Eugenia Paoletti
2. Ying Li
3. Danfeng Hong
4. Pedram Ghamisi
5. Shutao Li
6. Weiwei Song
7. Juan Mario Haut
8. Hyungtae Lee
9. Wei Li
10. Mengmeng Zhang

Papers from Yushi Chen's perspective

Ordered by how strongly the researchers Yushi Chen 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
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. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Spatial-Spectral Transformer for Hyperspectral Image Classification (2021) — 399 citations
• Deep Learning for Hyperspectral Image Classification: An Overview (2019) — 1,595 citations
• Generative Adversarial Networks for Hyperspectral Image Classification (2018) — 584 citations
• Advanced Spectral Classifiers for Hyperspectral Images: A review (2017) — 598 citations
• Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks (2016) — 2,659 citations
• Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network (2015) — 1,042 citations
• Deep Learning-Based Classification of Hyperspectral Data (2014) — 2,291 citations