Frédéric Ratle

Artificial Intelligence · 2008–2010

A researcher in the Science corpus: 3 papers; trusts 34 researchers and is trusted by 97 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 24 citations into it; #2,998 of 35,345 by trust here, #4,466 by citations
• Computer Vision and Pattern Recognition — 22 citations into it; #10,196 of 21,575 by trust here, #10,905 by citations
• Representation & Self-Supervised Learning — 20 citations into it; #787 of 3,145 by trust here, #668 by citations
• Media Technology — 17 citations into it; #1,379 of 3,621 by trust here, #1,171 by citations
• Analytical Chemistry — 8 citations into it
• Statistical Learning Theory — 8 citations into it
• Atmospheric Science — 6 citations into it; #2,403 of 4,804 by trust here, #2,974 by citations

Whom Frédéric Ratle trusts overall

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

1. L. Eon Bottou
2. Yann LeCun
3. Gustau Camps-Valls
4. Christopher K. I. Williams
5. Mikhail Belkin
6. Geoffrey E. Hinton
7. Ronan Collobert
8. Jason Aaron Edward Weston
9. Yoshua Bengio
10. Yuhai Wu

Who trusts Frédéric Ratle overall

1. Yoshua Bengio
2. Gao Huang
3. Quoc V. Le
4. Ruslan Salakhutdinov
5. Aaron C. Courville
6. Qizhe Xie
7. Sergey Levine
8. Dacheng Tao
9. Wenzhi Zhao
10. Trevor Darrell

Papers from Frédéric Ratle's perspective

Ordered by how strongly the researchers Frédéric Ratle 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Deep learning via semi-supervised embedding (2008) — 1,070 citations
• Semisupervised Neural Networks for Efficient Hyperspectral Image Classification (2010) — 323 citations
• Active Learning Methods for Remote Sensing Image Classification (2009) — 513 citations