Max Sommerfeld

Statistics and Probability · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 45 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 23 citations into it; #4,109 of 12,932 by trust here, #6,552 by citations
• Applied Mathematics — 15 citations into it; #2,260 of 7,620 by trust here, #3,410 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Computational Theory and Mathematics — 6 citations into it
• Signal Processing — 6 citations into it
• Biophysics — 5 citations into it
• Finance — 5 citations into it
• Molecular Biology — 5 citations into it

Whom Max Sommerfeld trusts overall

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

1. Werner Römisch
2. Marco Cuturi
3. Cédric Villani
4. Paul R. Rosenbaum
5. Patrick D. Schloss
6. Peter J. Bickel
7. Oliver Johnson
8. Martial Agueh
9. Yoav Benjamini
10. Nicolas Bonneel

Who trusts Max Sommerfeld overall

1. Victor M. Panaretos
2. Eustasio del Barrio
3. Yoav Zemel
4. Marc Hallin
5. Jean-Michel Loubes
6. Tudor Manole
7. Jing Lei
8. Gilles Mordant
9. Johan Segers
10. Carla Tameling

Papers from Max Sommerfeld's perspective

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

1. Topics in Optimal Transportation
2. Controlling the false discovery rate: a practical and powerful approach to multiple testing
3. THE GEOMETRY OF DISSIPATIVE EVOLUTION EQUATIONS: THE POROUS MEDIUM EQUATION
4. THE VARIATIONAL FORMULATION OF THE FOKKER-PLANCK EQUATION
5. The Earth Mover's Distance as a Metric for Image Retrieval
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Sinkhorn Distances: Lightspeed Computation of Optimal Transport
9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
10. ImageNet classification with deep convolutional neural networks

Their papers

• Empirical optimal transport on countable metric spaces: Distributional limits and statistical applications (2017) — 67 citations
• Inference for empirical Wasserstein distances on finite spaces (2016) — 185 citations