Tatyana V. Bandos Marsheva

Media Technology · 2007–2009

A researcher in the Science corpus: 2 papers; trusts 25 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Media Technology — 16 citations into it; #699 of 3,621 by trust here, #1,111 by citations
• Analytical Chemistry — 12 citations into it; #266 of 1,254 by trust here, #570 by citations
• Computer Vision and Pattern Recognition — 10 citations into it; #14,712 of 21,575 by trust here, #15,162 by citations
• Atmospheric Science — 9 citations into it; #932 of 4,804 by trust here, #2,716 by citations
• Statistical Learning Theory — 9 citations into it
• Artificial Intelligence — 6 citations into it; #23,878 of 35,345 by trust here, #23,077 by citations

Whom Tatyana V. Bandos Marsheva trusts overall

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

1. Gustau Camps-Valls
2. Eric R. Ziegel
3. R. Fisher
4. Vladimir Naumovich Vapnik
5. Lorenzo Bruzzone
6. Ron Kohavi
7. Bor-Chen Kuo
8. Christopher K. I. Williams
9. Peter N. Belhumeur
10. Matthew L. Clark

Who trusts Tatyana V. Bandos Marsheva overall

1. Gustau Camps-Valls
2. Jun Yu Li
3. Xiuping Jia
4. Frédéric Ratle
5. Yushi Chen
6. Guangrui Zhao
7. Qi Wang
8. Shutao Li
9. Xiaofei Yang
10. Behnood Rasti

Papers from Tatyana V. Bandos Marsheva's perspective

Ordered by how strongly the researchers Tatyana V. Bandos Marsheva 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Semi-Supervised Graph-Based Hyperspectral Image Classification (2007) — 613 citations
• Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis (2009) — 612 citations