Research Projects with Code at Cognitive Systems (Uni Bamberg)

Compilation of research projects with code conducted by the Cognitive Systems group of the Otto-Friedrich-Universität Bamberg. Also have a look inside our gitlab repository.

• Multi-Level-Multi-Modal-Explanation

  • Short description: Repository that contains the code for the paper "Explanation as a process: user-centric construction of multi-level and multi-modal explanations" submitted to KI 2021 by Finzel et al.
  • Researchers: Bettina Finzel, David Elias Tafler, Stephan Scheele, Ute Schmid
  • Link to paper: [link](to be updated)
  • Link to code: link

• Jupyter Notebooks of the Course XAI4Ing of the KI-Campus

  • LIME for Traffic Sign Classification Explanation link
  • LIME-Aleph link
  • Trepan link

• Expressive Explanations of DNNs by Combining Concept Analysis with ILP

  • Short description: Global, model specific symbolic explanations for classification results of black-box models.
  • Researchers: Johannes Rabold, Gesina Schwalbe, Ute Schmid
  • Link to paper: link
  • Link to code: link

• GeNME: Generating contrastive explanations for inductive logic programming based on a near miss approach

  • Short description: Near miss explanation generation for a positive example given a theory learned by ILP and a fact base.
  • Researchers: Johannes Rabold, Michael Siebers, Ute Schmid
  • Link to paper: link
  • Link to code: link

• LIME-Aleph

  • Short description: Local, model agnostic symbolic explanations for classification results of black-box models.
  • Researchers: Johannes Rabold, Hannah Deininger, Michael Siebers, Ute Schmid
  • Link to paper: link
  • Link to code: link

• Efficient Algorithms for Inductive Program Synthesis

  • Short description: Inductive program synthesis approach that guarantees to induce terminating programs with minimal generalization over examples.
  • Researchers: Emanuel Kitzelmann, Martin Hofmann, Ute Schmid
  • Link to paper: link
  • Link to code: link
  • Link to website: link

• Metagol_SN

  • Short description: Metagol is an inductive logic programming system learning logic rules from examples. Metagol_SN is a variant of that approach tailored to learning rules with exceptions. For example, any A is also a B unless it is also an C.
  • Researchers: Michael Siebers, Ute Schmid
  • Link to paper: link
  • Link to code: link

• SanSI

  • Short description: A system inducing definition for number series; like 1,3,5,7; by combining cognition-inspired enumeration and analytic fitting.
  • Researchers: Michael Siebers, Ute Schmid
  • Link to paper: link
  • Link to code: link
  • Link to website: link

• Reasoning WebAPI

  • Short description: A (roughly) RESTful Webservice classifying files as irrelevant and explaining the classification.
  • Researchers: Michael Siebers, Sebastian Seufert, Ute Schmid
  • Link to paper: link
  • Link to code: link
  • Link to website: link