Johannes Fürnkranz

Artificial Intelligence · 1998–2020

A researcher in the Science corpus: 22 papers; trusts 204 researchers and is trusted by 51 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 151 citations into it; #5,855 of 35,345 by trust here, #6,378 by citations
• Reinforcement Learning — 75 citations into it; #583 of 2,242 by trust here, #773 by citations
• Information Systems — 59 citations into it; #1,728 of 8,771 by trust here, #2,912 by citations
• Computer Vision and Pattern Recognition — 35 citations into it
• Statistical Learning Theory — 35 citations into it
• Evaluation & Benchmarks — 28 citations into it
• Optimization & Training — 27 citations into it
• Computational Theory and Mathematics — 26 citations into it

Whom Johannes Fürnkranz trusts overall

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

1. J. Ross Quinlan
2. William W. Cohen
3. Thomas G. Dietterich
4. Trevor J. Hastie
5. Peter Clark
6. Jorma Rissanen
7. Richard S. Sutton
8. Robert E. Schapire
9. Leo Breiman
10. Robert Tibshirani

Who trusts Johannes Fürnkranz overall

1. Ulrich Rückert
2. Eyke Hüllermeier
3. Krzysztof Dembczynski
4. Marjolein Fokkema
5. Min-Ling Zhang
6. Michael R. Berthold
7. Dmitry M. Malioutov
8. Christian Wirth
9. Jesse Read
10. Róbert Istvan Busa-Fekete

Papers from Johannes Fürnkranz's perspective

Ordered by how strongly the researchers Johannes Fürnkranz 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Preference-based reinforcement learning: a formal framework and a policy iteration algorithm (2012) — 177 citations
• Unsupervised generation of data mining features from linked open data (2012) — 197 citations
• Large-Scale Multi-label Text Classification - Revisiting Neural Networks (2013) — 384 citations
• Preference Learning (2010) — 315 citations
• Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning (2011) — 64 citations
• Label ranking by learning pairwise preferences (2008) — 589 citations
• Foundations of Rule Learning (2012) — 373 citations
• Decision Tree (2020) — 426 citations
• Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification (2017) — 184 citations
• A Survey of Preference-Based Reinforcement Learning Methods (2017) — 475 citations
• Using semantic similarity for multi-label zero-shot classification of text documents (2016) — 54 citations
• Model-Free Preference-Based Reinforcement Learning (2016) — 112 citations
• Preference-Based Reinforcement Learning: A Preliminary Survey (2013) — 30 citations
• Life Satisfaction, Household Income and Personality Traits (2012) — 535 citations
• Deep Belief Networks (2010) — 380 citations
• Frontiers in Artificial Intelligence and Applications (2009) — 556 citations
• From Local Patterns to Global Models: The LeGo Approach to Data Mining (2008) — 107 citations
• Multilabel classification via calibrated label ranking (2008) — 953 citations
• Round Robin Classification (2002) — 470 citations
• An Evaluation of Landmarking Variants (2001) — 98 citations