Charlotte Pelletier

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 84 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 42 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• Media Technology — 31 citations into it; #1,837 of 3,621 by trust here, #1,859 by citations
• Ecology — 29 citations into it; #4,149 of 10,717 by trust here, #7,011 by citations
• Signal Processing — 23 citations into it
• Optimization & Training — 14 citations into it
• Global and Planetary Change — 13 citations into it; #4,768 of 10,225 by trust here, #8,787 by citations
• Model Architectures — 13 citations into it

Whom Charlotte Pelletier trusts overall

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

1. Yoshua Bengio
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Ilya Sutskever
5. Leo Breiman
6. Anthony J. Bagnall
7. Sergey Ioffe
8. Xavier Glorot
9. Kaiming He
10. Christian Szegedy

Who trusts Charlotte Pelletier overall

1. Yuan Yuan
2. Marc Rußwurm
3. Lei Lin
4. Mariana Belgiu
5. Amrita Mohan
6. Benjamin Kellenberger
7. Sabeen Ahmed
8. Yezhen Cong
9. M. Wulder
10. Marco Körner

Papers from Charlotte Pelletier's perspective

Ordered by how strongly the researchers Charlotte Pelletier 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. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• InceptionTime: Finding AlexNet for time series classification (2019) — 1,670 citations
• Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series (2018) — 585 citations
• Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas (2016) — 555 citations