Bernd Gärtner

Computational Mechanics · 2007–2016

A researcher in the Science corpus: 6 papers; trusts 34 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Computational Mechanics — 17 citations into it; #2,624 of 10,001 by trust here, #5,567 by citations
• Statistics and Probability — 16 citations into it
• Optimization & Training — 14 citations into it; #2,307 of 2,601 by trust here, #2,519 by citations
• Artificial Intelligence — 12 citations into it; #11,129 of 35,345 by trust here, #23,077 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Statistical Learning Theory — 7 citations into it
• Numerical Analysis — 7 citations into it; #2,316 of 3,586 by trust here, #2,644 by citations

Whom Bernd Gärtner trusts overall

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

1. Robert Tibshirani
2. C. Burges
3. Kenneth L. Clarkson
4. Trevor J. Hastie
5. Saharon Rosset
6. Pankaj Kumar Agarwal
7. Ivor Wai-Hung Tsang
8. Sariel Har-Peled
9. Mihai Badoiu
10. Yoonkyung Lee

Who trusts Bernd Gärtner overall

1. Martin Jaggi
2. Simon Lacoste-Julien
3. Masayuki Karasuyama
4. Ricardo Ñanculef
5. Anton Osokin
6. Joachim Giesen
7. Tameem Adel
8. Ohad Shamir
9. Ichiro Takeuchi
10. Adriana Kovashka

Papers from Bernd Gärtner's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Learning Multiple Layers of Features from Tiny Images
9. © Institute of Mathematical Statistics, 2004 LEAST ANGLE REGRESSION
10. Support-Vector Networks

Their papers

• Optimization of Convex Functions with Random Pursuit (2011) — 58 citations
• Coresets for polytope distance (2009) — 72 citations
• Learning Sparse Additive Models with Interactions in High Dimensions (2016) — 16 citations
• Advances in Neural Information Processing Systems (NIPS) (2014) — 315 citations
• An Exponential Lower Bound on the Complexity of Regularization Paths (2009) — 38 citations
• Understanding and using linear programming (2007) — 447 citations