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Distributed Intelligence
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Model-based Reinforcement learning
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iCEM
Policy Extraction
Risk-Averse Planning
Model-free Reinforcement learning
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Object-centric Self-supervised Reinforcement Learning
Causal Reasoning in RL
Intrinsically Motivated Hierarchical Learner
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Representation Learning
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Optimizing Rank-based Metrics
Deep Graph Matching
CombOptNet
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Super-resolution Sensing for Haptics
Insight: a Haptic Sensor Powered by Vision and Machine Learning
ML for Science
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Predicting brain activity (fMRI)
Equation Learning for Statistical Physics
Machine Learning for Understanding Quantum Systems
Symbolic Regression and Equation Learning
Equation Learner for Extrapolation and Control
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Self-exploration of Behavior
Stepsize adaptation for stochastic optimization
The Playful Machine
Robust and Affordable Haptic Sensation with Sparse Sensor Configuration
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Prof. Dr. Georg Martius
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Dr. Christian Gumbsch
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