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Fundamental Methods for AI


Machine Learning for
Evolving Graph Data
Machine Learning
for Trusted AI


Machine Learning for Evolving Graph Data (Prof. Rahm)

  • Research is mostly restricted to static graphs, although most graphs change considerably over time
    • We need to compute embeddings that encode the temporal information - but Representation Learning for temporal graphs is largely unexplored
  • Goal:
    • Integration and research on foundations for temporal graphs and representation learning within GRADOOP
    • Build foundations of a graph streaming system that integrates methods for stream-based graph mining and learning
    • Investigate incremental graph mining and learning techniques such as graph sketches, incremental grouping, incremental representation learning and incremental frequent pattern mining
    • Application of graph stream analytics to be able to perform root cause analysis and anomaly detection


Neuromorphic Information processing for intelligent and cognitive data processing (Prof. Bogdan)

  • Real Neuromorphic Information Processing requires models closer to the neurological dynamical system
  • Integrate-and-Fire (IaF) neuron based Spiking Neural Networks (SNN) already outperform Deep Learning (DL) e.g. in recognition of handwritten patterns
    • SNN still lacks of reliable and universal training algorithms dyn. changes in the trained network are not yet possible
    • Synaptic plasticity is needed - not implemented in IaF neurons
  • We envison dynamic synapses, based on the Modified Stochastic Synaptic Model (MSSM) (K. Ellaithy and M.Bogdan 2017)




Privacy-Preserving Machine Learning (Prof. Rahm)

  • Goal: allow the analysis of person-related data with machine learning methods while guaranteeing a high degree of privacy such that the identity of individuals cannot be revealed
  • Challenging when person-related data from multiple sources is combined, e.g., user profiles, patient data involving clinical and genomic data
  • Requires to solve the problem of record linkage and a combination with ML-techniques


Argument-based Explanations for Trusted AI (Dr. habil. Baumann, Prof. Brewka)

  • Explainability of AI systems: not just a desirable add-on, but a legal requirement.
  • Strong recent focus in AI on computational models of argumentation:
    • Identify arguments and their relationships in texts or formal knowledge bases
    • Represent findings in argument graph; evaluate graph to identify reasonable argument sets
    • Accept their conclusions
  • Goal: exploit progress in the field to generate convincing explanations; options:
    • Translate representation formalism used to argument graph and benefit from the graph’s explanation facilities, or
    • Install an argumentation framework as a monitoring system for an underlying AI system