Christian Daniel

Reinforcement Learning · 2012–2018

A researcher in the Science corpus: 9 papers; trusts 101 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 116 citations into it; #761 of 2,242 by trust here, #916 by citations
• Artificial Intelligence — 111 citations into it; #8,822 of 35,345 by trust here, #9,465 by citations
• Control and Systems Engineering — 53 citations into it; #2,601 of 8,257 by trust here, #2,078 by citations
• Robotics & Control — 33 citations into it
• Cognitive Neuroscience — 26 citations into it
• Biomedical Engineering — 24 citations into it; #3,437 of 10,734 by trust here, #9,262 by citations
• Computational Theory and Mathematics — 19 citations into it; #11,377 of 12,182 by trust here, #4,696 by citations
• Probabilistic & Bayesian Methods — 18 citations into it

Whom Christian Daniel trusts overall

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

1. Jan Peters
2. Richard S. Sutton
3. Stefan Schaal
4. Andrew Barto
5. Auke Jan Ijspeert
6. Radford M. Neal
7. Marc Peter Deisenroth
8. Gerhard Neumann
9. Geoffrey E. Hinton
10. Nathan D. Ratliff

Who trusts Christian Daniel overall

1. Gerhard Neumann
2. Andras Gabor Kupcsik
3. Jan Peters
4. Marc Peter Deisenroth
5. Sergey Levine
6. Takayuki Osa
7. Pieter Abbeel
8. Kimin Lee
9. Herke Van Hoof
10. Jens Kober

Papers from Christian Daniel's perspective

Ordered by how strongly the researchers Christian Daniel 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. Reinforcement Learning: An Introduction
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Towards learning hierarchical skills for multi-phase manipulation tasks (2015) — 136 citations
• Active Reward Learning (2014) — 129 citations
• Learning Step Size Controllers for Robust Neural Network Training (2016) — 96 citations
• Active reward learning with a novel acquisition function (2015) — 69 citations
• Learning concurrent motor skills in versatile solution spaces (2012) — 44 citations
• Probabilistic Recurrent State-Space Models (2018) — 149 citations
• Hierarchical Relative Entropy Policy Search (2014) — 252 citations
• Information-Theoretic Motor Skill Learning (2013) — 8 citations
• Probabilistic Movement Primitives (2013) — 728 citations