Sören Sonnenburg

Artificial Intelligence · 2006–2011

A researcher in the Science corpus: 12 papers; trusts 62 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it; #5,275 of 35,345 by trust here, #10,319 by citations
• Computer Vision and Pattern Recognition — 36 citations into it; #5,625 of 21,575 by trust here, #10,334 by citations
• Statistical Learning Theory — 35 citations into it
• Optimization & Training — 33 citations into it
• Molecular Biology — 26 citations into it; #5,479 of 17,773 by trust here, #6,960 by citations
• Statistics and Probability — 16 citations into it
• Computational Mechanics — 11 citations into it; #3,091 of 10,001 by trust here, #6,877 by citations
• Representation & Self-Supervised Learning — 9 citations into it

Whom Sören Sonnenburg trusts overall

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

1. Thorsten Joachims
2. Bernhard Schölkopf
3. Alex Smola
4. Olivier Chapelle
5. Francis R. Bach
6. Chih-Jen Lin
7. Vladimir Naumovich Vapnik
8. Gert R. G. Lanckriet
9. Rong-En Fan
10. Geoffrey E. Hinton

Who trusts Sören Sonnenburg overall

1. Marius Kloft
2. Christian Widmer
3. Alexander Binder
4. Marina M.-C. Vidovic
5. Alain Rakotomamonjy
6. Alexander Zien
7. Francis R. Bach
8. Chang Xu
9. Emilio Carrizosa
10. Gunnar Rätsch

Papers from Sören Sonnenburg's perspective

Ordered by how strongly the researchers Sören Sonnenburg 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• l p -Norm Multiple Kernel Learning (2011) — 356 citations
• Optimized cutting plane algorithm for support vector machines (2008) — 133 citations
• lp-Norm Multiple Kernel Learning (2011) — 338 citations
• Support Vector Machines and Kernels for Computational Biology (2008) — 771 citations
• POIMs: positional oligomer importance matrices—understanding support vector machine-based signal detectors (2008) — 73 citations
• Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation (2011) — 49 citations
• The SHOGUN Machine Learning Toolbox (2010) — 306 citations
• The Feature Importance Ranking Measure (2009) — 169 citations
• Optimized Cutting Plane Algorithm for Large-Scale Risk Minimization (2009) — 86 citations
• Efficient and Accurate Lp-Norm Multiple Kernel Learning (2009) — 260 citations
• The Need for Open Source Software in Machine Learning (2007) — 244 citations
• Large Scale Multiple Kernel Learning (2006) — 1,427 citations