Mercedes Eugenia Paoletti

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 302 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 139 citations into it
• Media Technology — 131 citations into it; #1,459 of 3,621 by trust here, #839 by citations
• Artificial Intelligence — 92 citations into it
• Atmospheric Science — 76 citations into it; #2,572 of 4,804 by trust here, #1,705 by citations
• Model Architectures — 49 citations into it
• Analytical Chemistry — 30 citations into it
• Optimization & Training — 30 citations into it
• Ecology — 22 citations into it

Whom Mercedes Eugenia Paoletti trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Yann LeCun
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Kaiming He
7. Sergey Ioffe
8. Ilya Sutskever
9. Karen Simonyan
10. Ross B. Girshick

Who trusts Mercedes Eugenia Paoletti overall

1. Swalpa Kumar Roy
2. Muhammad Ahmad
3. Guangrui Zhao
4. Suvojit Manna
5. Shutao Li
6. Juan Mario Haut
7. Xin He
8. Tiecheng Song
9. Bing Liu
10. Shervin Minaee

Papers from Mercedes Eugenia Paoletti's perspective

Ordered by how strongly the researchers Mercedes Eugenia Paoletti 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

• Capsule Networks for Hyperspectral Image Classification (2019) — 311 citations
• Deep Pyramidal Residual Networks for Spectral–Spatial Hyperspectral Image Classification (2019) — 428 citations
• Deep learning classifiers for hyperspectral imaging: A review (2019) — 930 citations
• A new deep convolutional neural network for fast hyperspectral image classification (2017) — 563 citations