Jérôme Louradour

Artificial Intelligence · 2009–2014

A researcher in the Science corpus: 4 papers; trusts 77 researchers and is trusted by 1,145 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #410 of 35,345 by trust here, #1,000 by citations
• Computer Vision and Pattern Recognition — 31 citations into it; #4,099 of 21,575 by trust here, #7,249 by citations
• Representation & Self-Supervised Learning — 23 citations into it
• Model Architectures — 19 citations into it; #283 of 2,278 by trust here, #431 by citations
• Probabilistic & Bayesian Methods — 18 citations into it
• Natural Language Processing — 13 citations into it
• Signal Processing — 13 citations into it
• Generative Models — 6 citations into it

Whom Jérôme Louradour trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Alex Graves
4. J. Urgen Schmidhuber
5. Yann LeCun
6. Ruslan Salakhutdinov
7. Hugo Larochelle
8. Alex Krizhevsky
9. Sepp Hochreiter
10. Radford M. Neal

Who trusts Jérôme Louradour overall

1. Yoshua Bengio
2. Ruslan Salakhutdinov
3. Abhinav Kumar Gupta
4. Mohit Bansal
5. Xinlei Chen
6. Quoc V. Le
7. Sergey Levine
8. Trevor Darrell
9. Lu Jiang
10. Dacheng Tao

Papers from Jérôme Louradour's perspective

Ordered by how strongly the researchers Jérôme Louradour 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. 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Curriculum learning (2009) — 7,325 citations
• Dropout Improves Recurrent Neural Networks for Handwriting Recognition (2013) — 587 citations
• Exploring Strategies for Training Deep Neural Networks (2009) — 1,259 citations
• The A2iA Arabic Handwritten Text Recognition System at the Open HaRT2013 Evaluation (2014) — 48 citations