Jean‐Michel Poggi

Statistics and Probability · 2006–2021

A researcher in the Science corpus: 6 papers; trusts 42 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 55 citations into it; #5,564 of 12,932 by trust here, #4,014 by citations
• Artificial Intelligence — 47 citations into it; #13,455 of 35,345 by trust here, #13,353 by citations
• Computer Vision and Pattern Recognition — 27 citations into it; #8,015 of 21,575 by trust here, #7,750 by citations
• Statistical Learning Theory — 19 citations into it
• Molecular Biology — 17 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Information Systems — 13 citations into it; #7,093 of 8,771 by trust here, #6,770 by citations
• Computational Theory and Mathematics — 8 citations into it

Whom Jean‐Michel Poggi trusts overall

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

1. Leo Breiman
2. Stéphane Mallat
3. Trevor J. Hastie
4. David L. Donoho
5. Jason Aaron Edward Weston
6. Isabelle M Guyon
7. Jianqing Fan
8. Ingrid Daubechies
9. Niko Speybroeck
10. U. Groemping

Who trusts Jean‐Michel Poggi 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. Jacinta Chan Phooi M’ng

Papers from Jean‐Michel Poggi's perspective

Ordered by how strongly the researchers Jean‐Michel Poggi 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. ImageNet: A large-scale hierarchical image database
6. Random Forests
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Regression Shrinkage and Selection via the Lasso
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

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 global sensitivity analysis: A selective review (2021) — 307 citations
• Random Forests for Big Data (2015) — 357 citations
• Wavelets and their applications (2007) — 477 citations
• Multivariate denoising using wavelets and principal component analysis (2006) — 206 citations