Stefan Rüping

Statistical Learning Theory · 2001–2019

A researcher in the Science corpus: 3 papers; trusts 17 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Statistical Learning Theory — 12 citations into it
• Artificial Intelligence — 10 citations into it; #3,647 of 35,345 by trust here, #10,566 by citations
• Statistics and Probability — 8 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it; #5,167 of 21,575 by trust here, #10,905 by citations
• Information Systems — 6 citations into it
• Information Theory — 5 citations into it

Whom Stefan Rüping trusts overall

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

1. Vladimir Naumovich Vapnik
2. Yuhai Wu
3. Jorma Rissanen
4. Thorsten Joachims
5. Bernhard Schölkopf
6. J. Ross Quinlan
7. Christophe G. Giraud-Carrier
8. Huan Liu
9. Alex Smola
10. C. Burges

Who trusts Stefan Rüping overall

1. Cynthia Rudin
2. Berk Ustun
3. Alexander Rainer Tassilo Gepperth
4. Adrien Bibal
5. Edoardo Vignotto
6. D. V. Carvalho
7. Anupam Datta
8. Jun Yang
9. Ye Xiang
10. Ali Ayub

Papers from Stefan Rüping's perspective

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

Their papers

• Incremental learning with support vector machines (2001) — 599 citations
• A review of machine learning for the optimization of production processes (2019) — 304 citations
• Learning interpretable models (2006) — 107 citations