Thomas A. Runkler

Reinforcement Learning · 2016–2020

A researcher in the Science corpus: 8 papers; trusts 124 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 92 citations into it
• Artificial Intelligence — 74 citations into it; #10,372 of 35,345 by trust here, #14,438 by citations
• Control and Systems Engineering — 17 citations into it; #3,277 of 8,257 by trust here, #4,514 by citations
• Probabilistic & Bayesian Methods — 17 citations into it
• Computational Theory and Mathematics — 15 citations into it
• Electrical and Electronic Engineering — 14 citations into it
• Optimization & Training — 13 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it

Whom Thomas A. Runkler trusts overall

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

1. Richard S. Sutton
2. Martin A. Riedmiller
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Geoffrey J. Gordon
6. Ronald J. Williams
7. J. Urgen Schmidhuber
8. Yoshua Bengio
9. Stefan Depeweg
10. Andrew Barto

Who trusts Thomas A. Runkler overall

1. Ramitha Sundar
2. Yashesh D. Dhebar
3. Daniel Hein
4. Steffen Udluft
5. Lucy R. Chai
6. Javier Antorán Cabiscol
7. Jorge Armando Mendez Mendez
8. Phillip Swazinna

Papers from Thomas A. Runkler's perspective

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

Their papers

• Overcoming Model Bias for Robust Offline Deep Reinforcement Learning (2020) — 95 citations
• A benchmark environment motivated by industrial control problems (2017) — 68 citations
• Batch reinforcement learning on the industrial benchmark: First experiences (2017) — 12 citations
• Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies (2016) — 89 citations
• Neural Topic Modeling with Continual Lifelong Learning (2020) — 56 citations
• Interpretable Dynamics Models for Data-efficient Reinforcement Learning (2019) — 4 citations
• Sensitivity analysis for predictive uncertainty (2017) — 8 citations
• Interpretable Policies for Reinforcement Learning by Genetic Programming (2017) — 156 citations