Sen Jia

Media Technology · IEEE Transactions on Geoscience and Remote Sensing · 2009–2021

A researcher in the Science corpus: 5 papers; trusts 64 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Media Technology — 67 citations into it; #1,238 of 3,621 by trust here, #1,135 by citations
• Computer Vision and Pattern Recognition — 49 citations into it; #17,173 of 21,575 by trust here, #14,822 by citations
• Atmospheric Science — 35 citations into it; #2,005 of 4,804 by trust here, #2,621 by citations
• Artificial Intelligence — 26 citations into it
• Analytical Chemistry — 23 citations into it; #676 of 1,254 by trust here, #837 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Signal Processing — 14 citations into it
• Biomedical Engineering — 13 citations into it

Whom Sen Jia trusts overall

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

1. Patrik O. Hoyer
2. Chein-I. Chang
3. Jose M. P. Nascimento
4. Daniel D. Lee
5. José M. Bioucas-Dias
6. S. Li
7. Lidan Miao
8. Daniel C. Heinz
9. Aapo Hyvärinen
10. Geoffrey E. Hinton

Who trusts Sen Jia overall

1. Y. Qian
2. Nicolas Gillis
3. José M. Bioucas-Dias
4. Xiaoqiang Lu
5. Muhammad Ahmad
6. Xiaohui Yuan
7. Marian-Daniel Iordache
8. Jun Zhou
9. Antonio J. Plaza
10. Xiaodong Xu

Papers from Sen Jia's perspective

Ordered by how strongly the researchers Sen Jia trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection (2016) — 340 citations
• A survey: Deep learning for hyperspectral image classification with few labeled samples (2021) — 346 citations
• Convolutional neural networks for hyperspectral image classification (2017) — 609 citations
• Hyperspectral Unmixing via $L_{1/2}$ Sparsity-Constrained Nonnegative Matrix Factorization (2011) — 558 citations
• Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing (2009) — 394 citations