Christophe G. Giraud-Carrier

Artificial Intelligence · 1998–2018

A researcher in the Science corpus: 9 papers; trusts 19 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #17,100 of 35,345 by trust here, #21,878 by citations
• Information Systems — 14 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Statistics and Probability — 9 citations into it
• Representation & Self-Supervised Learning — 6 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Christophe G. Giraud-Carrier trusts overall

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

1. Paul E. Utgoff
2. J. Urgen Schmidhuber
3. Pavel Brazdil
4. David Hilton Wolpert
5. Gonzalo Navarro
6. João Gama
7. Ricardo Vilalta
8. R. Caruana
9. Sebastian Thrun
10. Johannes Fürnkranz

Who trusts Christophe G. Giraud-Carrier overall

1. Ricardo Vilalta
2. Hongyu Yang
3. Benjamin Letham
4. Cynthia Rudin
5. Frank Hutter
6. Silvio B. Guerra
7. Matthias Feurer
8. Stefan Rüping
9. Katharina Eggensperger
10. Jan N. van Rijn

Papers from Christophe G. Giraud-Carrier's perspective

Ordered by how strongly the researchers Christophe G. Giraud-Carrier 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. Multitask Learning
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Introduction to the Special Issue on Meta-Learning (2004) — 184 citations
• Preprocessor Selection for Machine Learning Pipelines (2018) — 13 citations
• Encyclopedia of Machine Learning and Data Mining (2017) — 452 citations
• An instance level analysis of data complexity (2014) — 437 citations
• Improving the Study of Campaign Contributors with Record Linkage (2010)
• Meta-Learning (2009) — 212 citations
• Metalearning - A Tutorial (2008) — 40 citations
• Using Meta-Learning to Support Data Mining (2004) — 393 citations
• Beyond predictive accuracy : what? (1998)