Andre Lemme
Cognitive Neuroscience · 2013–2015
A researcher in the Science corpus: 5 papers; trusts 14 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Cognitive Neuroscience — 34 citations into it; #7,247 of 10,852 by trust here, #7,600 by citations
- Control and Systems Engineering — 29 citations into it; #2,792 of 8,257 by trust here, #4,262 by citations
- Artificial Intelligence — 22 citations into it; #26,114 of 35,345 by trust here, #25,809 by citations
- Cellular and Molecular Neuroscience — 20 citations into it
- Biomedical Engineering — 16 citations into it
- Electrical and Electronic Engineering — 12 citations into it; #10,746 of 16,539 by trust here, #14,379 by citations
- Reinforcement Learning — 8 citations into it
- Probabilistic & Bayesian Methods — 5 citations into it
Whom Andre Lemme trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Seyed Mohammad Khansari-Zadeh
- Stefan Schaal
- 採編典藏組
- Aude Gemma Billard
- Auke Jan Ijspeert
- Andrea Soltoggio
- Sylvain Calinon
- K. Zadeh
- Klaus Neumann
- Richard S. Sutton
Who trusts Andre Lemme overall
- Yaron Meirovitch
- Harish Chaandar Ravichandar
- Klaus Neumann
- Daniel Bennequin
- Andrea Soltoggio
- Iman Salehi
- Tamar Flash
- Muhammad Asif Rana
- Takayuki Osa
- Jochen J. Steil
Papers from Andre Lemme's perspective
Ordered by how strongly the researchers Andre Lemme trusts endorse each paper. Same corpus, a different order for every researcher.
- Reinforcement Learning: An Introduction
- Is imitation learning the route to humanoid robots?
- Learning Attractor Landscapes for Learning Motor Primitives
- Et al
- Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors
- Task-Specific Generalization of Discrete and Periodic Dynamic Movement Primitives
- BM: An iterative algorithm to learn stable non-linear dynamical systems with Gaussian mixture models
- A Neural Substrate of Prediction and Reward
- Movement imitation with nonlinear dynamical systems in humanoid robots
- GENERATIVE ADVERSARIAL NETS
Their papers
- Open-source benchmarking for learned reaching motion generation in robotics (2015) — 43 citations
- Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances (2013) — 20 citations
- Neural learning of vector fields for encoding stable dynamical systems (2014) — 56 citations
- Learning the rules of a game: Neural conditioning in human-robot interaction with delayed rewards (2013) — 11 citations
- Neural learning of stable dynamical systems based on data-driven Lyapunov candidates (2013) — 61 citations