Robin Genuer

Statistics and Probability · 2010–2015

A researcher in the Science corpus: 4 papers; trusts 45 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 39 citations into it; #5,563 of 12,932 by trust here, #4,014 by citations
• Artificial Intelligence — 32 citations into it; #13,453 of 35,345 by trust here, #13,353 by citations
• Statistical Learning Theory — 24 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it; #8,794 of 21,575 by trust here, #9,187 by citations
• Evaluation & Benchmarks — 13 citations into it
• Molecular Biology — 12 citations into it
• Information Systems — 11 citations into it; #7,095 of 8,771 by trust here, #6,770 by citations
• Model Architectures — 7 citations into it

Whom Robin Genuer trusts overall

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

1. Leo Breiman
2. Niko Speybroeck
3. Gérard Biau
4. Trevor J. Hastie
5. Jason Aaron Edward Weston
6. Jianqing Fan
7. Isabelle M Guyon
8. U. Groemping
9. Adele Cutler
10. Andre´ Elisseeff

Who trusts Robin Genuer overall

1. Hristos Tyralis
2. Maximilian Pichler
3. Jonathan Brophy
4. Hannes Feilhauer
5. Hong Han
6. Jaime Lynn Speiser
7. Jintao Ke
8. M. L. N. L. Nelson Sanchez-Pinto
9. Yuanyuan Gao
10. Yichen Zhou

Papers from Robin Genuer's perspective

Ordered by how strongly the researchers Robin Genuer trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Regression Shrinkage and Selection via the Lasso
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Random Forests

Their papers

• Variable selection using random forests (2010) — 1,812 citations
• VSURF: An R Package for Variable Selection Using Random Forests (2015) — 550 citations
• Random Forests for Big Data (2015) — 357 citations
• Analysis of purely random forests bias (2014) — 81 citations