12 citations · 17 across the 5 of their papers we have counts for
5 papers
When a Relation Tells More Than a Concept: Exploring and Evaluating Classifier Decisions with CoReX
Bettina Finzel, Patrick Hilme, Johannes Rabold +1
Explanations for Convolutional Neural Networks (CNNs) based on relevance of input pixels might be too unspecific to evaluate which and how input features impact model decisions. Es…
Generating Contrastive Explanations for Inductive Logic Programming Based on a Near Miss Approach
Johannes Rabold, Michael Siebers, Ute Schmid
In recent research, human-understandable explanations of machine learning models have received a lot of attention. Often explanations are given in form of model simplifications or…
Expressive Explanations of DNNs by Combining Concept Analysis with ILP
Johannes Rabold, Gesina Schwalbe, Ute Schmid
Explainable AI has emerged to be a key component for black-box machine learning approaches in domains with a high demand for reliability or transparency. Examples are medical assis…
Effect of Superpixel Aggregation on Explanations in LIME -- A Case Study with Biological Data
Ludwig Schallner, Johannes Rabold, Oliver Scholz +1
End-to-end learning with deep neural networks, such as convolutional neural networks (CNNs), has been demonstrated to be very successful for different tasks of image classification…
Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph
Johannes Rabold, Hannah Deininger, Michael Siebers +1
With the increasing number of deep learning applications, there is a growing demand for explanations. Visual explanations provide information about which parts of an image are rele…