76 citations · 205 across the 15 of their papers we have counts for
4 papers · 1 filter
Relate to Predict: Towards Task-Independent Knowledge Representations for Reinforcement Learning
Thomas Schnürer, Malte Probst, Horst-Michael Gross
Reinforcement Learning (RL) can enable agents to learn complex tasks. However, it is difficult to interpret the knowledge and reuse it across tasks. Inductive biases can address su…
Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments
Daniel Seichter, Söhnke Benedikt Fischedick, Mona Köhler +1
Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about indi…
Improving Predictive Performance and Calibration by Weight Fusion in Semantic Segmentation
Timo Sämann, Ahmed Mostafa Hammam, Andrei Bursuc +2
Averaging predictions of a deep ensemble of networks is apopular and effective method to improve predictive performance andcalibration in various benchmarks and Kaggle competitions…
Efficient and Robust Semantic Mapping for Indoor Environments
Daniel Seichter, Patrick Langer, Tim Wengefeld +3
A key proficiency an autonomous mobile robot must have to perform high-level tasks is a strong understanding of its environment. This involves information about what types of objec…