Ulrich Rückert

Artificial Intelligence · 2006–2018

A researcher in the Science corpus: 4 papers; trusts 46 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 36 citations into it; #14,171 of 35,345 by trust here, #16,471 by citations
• Statistical Learning Theory — 20 citations into it
• Algorithms & Complexity — 11 citations into it
• Statistics and Probability — 10 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Information Systems — 9 citations into it; #3,929 of 8,771 by trust here, #5,713 by citations
• Optimization & Training — 9 citations into it
• Evaluation & Benchmarks — 8 citations into it

Whom Ulrich Rückert trusts overall

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

1. William W. Cohen
2. J. Ross Quinlan
3. Jaka Demsar
4. Johannes Fürnkranz
5. Jerome H. Friedman
6. Vladimir Naumovich Vapni
7. Peter Clark
8. Robert E. Schapire
9. Yoav Freund
10. Robert Craig Holte

Who trusts Ulrich Rückert overall

1. Cynthia Rudin
2. Peter R. Rijnbeek
3. Kush R. Varshney
4. Dmitry M. Malioutov
5. Ş. Ertekin
6. Izzeddin Gur
7. Jan A. Kors
8. Jerome H. Friedman
9. Geunwoo Kim
10. Sanjeeb Dash

Papers from Ulrich Rückert's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. Gradient-based learning applied to document recognition
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Learning to Navigate the Web (2018) — 78 citations
• An experimental evaluation of simplicity in rule learning (2008) — 20 citations
• Margin-based first-order rule learning (2007) — 29 citations
• A statistical approach to rule learning (2006) — 29 citations