Kurt Driessens

Reinforcement Learning · 2001–2014

A researcher in the Science corpus: 8 papers; trusts 46 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 44 citations into it; #436 of 2,242 by trust here, #1,155 by citations
• Artificial Intelligence — 27 citations into it; #5,595 of 35,345 by trust here, #12,083 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Information Systems — 6 citations into it
• Statistical Learning Theory — 6 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it; #4,476 of 12,182 by trust here, #7,142 by citations

Whom Kurt Driessens trusts overall

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

1. Leslie Pack Kaelbling
2. Hendrik Blockeel
3. J. Ramon
4. Richard S. Sutton
5. David Chapman
6. William D. Smart
7. Ben J. A. Kröse
8. Andrew Barto
9. Thomas Gärtner
10. Michael L. Littman

Who trusts Kurt Driessens overall

1. Kristian Kersting
2. Martijn van Otterlo
3. Srijita Das
4. Sriraam Natarajan
5. Prasad Tadepalli
6. Thomas Gärtner
7. Robert Givan
8. Javier García
9. Vinícius Flores Zambaldi
10. Marta Garnelo

Papers from Kurt Driessens's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Reinforcement Learning: An Introduction
6. Long Short-Term Memory
7. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet: A large-scale hierarchical image database

Their papers

• Integrating Guidance into Relational Reinforcement Learning (2004) — 106 citations
• Automatically Mapped Transfer between Reinforcement Learning Tasks via Three-Way Restricted Boltzmann Machines (2013) — 26 citations
• Graph kernels and Gaussian processes for relational reinforcement learning (2006) — 109 citations
• An automated measure of MDP similarity for transfer in reinforcement learning (2014) — 103 citations
• Non-parametric policy gradients: a unified treatment of propositional and relational domains (2008) — 76 citations
• Relational Instance Based Regression for Relational Reinforcement Learning (2003) — 85 citations
• Relational reinforcement learning (2001) — 360 citations
• Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner (2001) — 96 citations