Andreas Christmann

Artificial Intelligence · 2001–2011

A researcher in the Science corpus: 7 papers; trusts 53 researchers and is trusted by 271 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it; #1,607 of 35,345 by trust here, #3,348 by citations
• Statistical Learning Theory — 46 citations into it; #86 of 1,038 by trust here, #79 by citations
• Computer Vision and Pattern Recognition — 40 citations into it; #2,371 of 21,575 by trust here, #2,536 by citations
• Statistics and Probability — 27 citations into it; #2,615 of 12,932 by trust here, #3,609 by citations
• Control and Systems Engineering — 16 citations into it; #3,018 of 8,257 by trust here, #5,490 by citations
• Optimization & Training — 16 citations into it
• Computational Mechanics — 15 citations into it; #2,223 of 10,001 by trust here, #4,315 by citations
• Information Systems — 8 citations into it

Whom Andreas Christmann trusts overall

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

1. Bernhard Schölkopf
2. Ingo Steinwart
3. Alex Smola
4. Christopher K. I. Williams
5. Peter J. Rousseeuw
6. Kenji Fukumizu
7. Olivier Bousquet
8. Matthias Hein
9. Peter L. Bartlett
10. Vladimir Naumovich Vapnik

Who trusts Andreas Christmann overall

1. Bharath K. Sriperumbudur
2. Arthur Gretton
3. Kenji Fukumizu
4. Ingo Steinwart
5. Zoltán Szabó
6. Battista Biggio
7. Bernhard Schölkopf
8. Krikamol Muandet
9. Aniket Anand Deshmukh
10. Gilles Blanchard

Papers from Andreas Christmann's perspective

Ordered by how strongly the researchers Andreas Christmann 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Support vector machines (2008) — 8,507 citations
• Measuring overlap in binary regression (2001) — 65 citations
• Estimating conditional quantiles with the help of the pinball loss (2011) — 295 citations
• On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition (2004) — 150 citations
• Robust estimation of Cronbach's alpha (2006) — 406 citations
• Universal Kernels on Non-Standard Input Spaces (2010) — 115 citations
• Sparsity of SVMs that use the epsilon-insensitive loss (2008) — 15 citations