Marco A. Wiering

Reinforcement Learning · 1996–2018

A researcher in the Science corpus: 17 papers; trusts 264 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 108 citations into it; #662 of 2,242 by trust here, #850 by citations
• Artificial Intelligence — 40 citations into it; #5,098 of 35,345 by trust here, #6,569 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Generative Models — 15 citations into it
• Statistical Learning Theory — 14 citations into it
• Model Architectures — 8 citations into it
• Mathematical Optimization — 7 citations into it
• Control and Systems Engineering — 6 citations into it; #1,530 of 8,257 by trust here, #4,262 by citations

Whom Marco A. Wiering trusts overall

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

1. Richard S. Sutton
2. Satinder Singh
3. J. Urgen Schmidhuber
4. Andrew Barto
5. Michael L. Littman
6. Ben J. A. Kröse
7. Leslie Pack Kaelbling
8. Longxin Lin
9. Geoffrey E. Hinton
10. Gavin Adrian Rummery

Who trusts Marco A. Wiering overall

1. J. Urgen Schmidhuber
2. Hua Wei
3. Hado van Hasselt
4. Ammar Haydari
5. Dongbin Zhao
6. Alexander L. Strehl
7. Yoshua Bengio
8. van der D David Pol
9. Sergey Levine
10. Tianshu Chu

Papers from Marco A. Wiering's perspective

Ordered by how strongly the researchers Marco A. Wiering 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Reinforcement Learning in Continuous Action Spaces (2007) — 249 citations
• A theoretical and empirical analysis of Expected Sarsa (2009) — 245 citations
• An analysis of rotation matrix and colour constancy data augmentation in classifying images of animals (2018) — 35 citations
• Artificial Intelligence (2017) — 3,274 citations
• Using continuous action spaces to solve discrete problems (2009) — 72 citations
• Comparing Generative Adversarial Network Techniques for Image Creation and Modification (2018) — 13 citations
• Reinforcement learning in the game of Othello: Learning against a fixed opponent and learning from self-play (2013) — 65 citations
• Reinforcement Learning and Markov Decision Processes (2012) — 534 citations
• Exploiting Best-Match Equations for Efficient Reinforcement Learning (2011) — 18 citations
• Multi-Agent Reinforcement Learning for Traffic Light control (2000) — 308 citations
• Multi-Agent Reinforcement Leraning for Traffic Light Control (2000) — 305 citations
• Efficient model-based exploration (1998)
• Fast Online Q(λ) (1998)
• HQ-Learning (1997)
• Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement (1997)
• Solving POMDPs with Levin Search and EIRA (1996)
• Simple Principles of Metalearning (1996)