79 citations · 370 across the 33 of their papers we have counts for
9 papers · 1 filter
Combining Sub-Symbolic and Symbolic Methods for Explainability
Anna Himmelhuber, Stephan Grimm, Sonja Zillner +3
Similarly to other connectionist models, Graph Neural Networks (GNNs) lack transparency in their decision-making. A number of sub-symbolic approaches have been developed to provide…
Measuring Data Quality for Dataset Selection in Offline Reinforcement Learning
Phillip Swazinna, Steffen Udluft, Thomas Runkler
Recently developed offline reinforcement learning algorithms have made it possible to learn policies directly from pre-collected datasets, giving rise to a new dilemma for practiti…
Ontology-Based Skill Description Learning for Flexible Production Systems
Anna Himmelhuber, Stephan Grimm, Thomas Runkler +1
The increasing importance of resource-efficient production entails that manufacturing companies have to create a more dynamic production environment, with flexible manufacturing ma…
Demystifying Graph Neural Network Explanations
Anna Himmelhuber, Mitchell Joblin, Martin Ringsquandl +1
Graph neural networks (GNNs) are quickly becoming the standard approach for learning on graph structured data across several domains, but they lack transparency in their decision-m…
Towards Data-Free Domain Generalization
Ahmed Frikha, Haokun Chen, Denis Krompaß +2
In this work, we investigate the unexplored intersection of domain generalization (DG) and data-free learning. In particular, we address the question: How can knowledge contained i…
Learning through structure: towards deep neuromorphic knowledge graph embeddings
Victor Caceres Chian, Marcel Hildebrandt, Thomas Runkler +1
Computing latent representations for graph-structured data is an ubiquitous learning task in many industrial and academic applications ranging from molecule synthetization to socia…