Thomas Gärtner

Artificial Intelligence · 2002–2006

A researcher in the Science corpus: 6 papers; trusts 40 researchers and is trusted by 96 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it; #7,098 of 35,345 by trust here, #11,192 by citations
• Statistical Learning Theory — 23 citations into it; #207 of 1,038 by trust here, #159 by citations
• Graph Learning — 10 citations into it; #132 of 693 by trust here, #201 by citations
• Information Systems — 10 citations into it; #1,813 of 8,771 by trust here, #3,098 by citations
• Molecular Biology — 9 citations into it
• Computer Vision and Pattern Recognition — 7 citations into it; #4,010 of 21,575 by trust here, #10,500 by citations
• Reinforcement Learning — 7 citations into it
• Computer Networks and Communications — 5 citations into it

Whom Thomas Gärtner trusts overall

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

1. Bernhard Schölkopf
2. Alex Smola
3. David Haussler
4. Kurt Driessens
5. Vladimir Naumovich Vapni
6. Tommi S. Jaakkola
7. Michael Collins
8. Bernhard E. Boser
9. Hisashi Kashima
10. Hendrik Blockeel

Who trusts Thomas Gärtner overall

1. Masashi Sugiyama
2. Karsten M. Borgwardt
3. Taiji Suzuki
4. Zhi-Hua Zhou
5. Nils M. Kriege
6. Saurabh Verma
7. S. V. N. Vishwanathan
8. Prasad Tadepalli
9. Risi Kondor
10. Kristen Grauman

Papers from Thomas Gärtner's perspective

Ordered by how strongly the researchers Thomas Gärtner 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A survey of kernels for structured data (2003) — 509 citations
• Cyclic pattern kernels for predictive graph mining (2004) — 377 citations
• Efficient co-regularised least squares regression (2006) — 179 citations
• Graph kernels and Gaussian processes for relational reinforcement learning (2006) — 109 citations
• On Graph Kernels: Hardness Results and Efficient Alternatives (2003) — 1,062 citations
• Multi-Instance Kernels (2002) — 574 citations