Xing Zhao

Computer Vision and Pattern Recognition · with Chen · 2014–2015

A researcher in the Science corpus: 2 papers; trusts 18 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 23 citations into it; #4,441 of 21,575 by trust here, #4,030 by citations
• Media Technology — 22 citations into it; #351 of 3,621 by trust here, #240 by citations
• Artificial Intelligence — 17 citations into it
• Atmospheric Science — 13 citations into it; #218 of 4,804 by trust here, #293 by citations
• Probabilistic & Bayesian Methods — 9 citations into it
• Ecology — 6 citations into it
• Model Architectures — 6 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Xing Zhao trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Yann LeCun
4. Ruslan Salakhutdinov
5. Alex Krizhevsky
6. Kunihiko Fukushima
7. Ilya Sutskever
8. Radford M. Neal
9. Simon Osindero
10. Diederik P. Kingma

Who trusts Xing Zhao overall

1. Ying Li
2. Mengmeng Zhang
3. Liangpei Zhang
4. Wei Li
5. Jingxiang Yang
6. Qian Du
7. Francois P. S. Luus
8. Mercedes Eugenia Paoletti
9. Andreas Kamilaris
10. Danfeng Hong

Papers from Xing Zhao's perspective

Ordered by how strongly the researchers Xing 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• 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