1 citations · 1 across the 2 of their papers we have counts for
4 papers
Beneficial and Harmful Explanatory Machine Learning
Lun Ai, Stephen H. Muggleton, Céline Hocquette +2
Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this con…
Verifying Deep Learning-based Decisions for Facial Expression Recognition
Ines Rieger, Rene Kollmann, Bettina Finzel +2
Neural networks with high performance can still be biased towards non-relevant features. However, reliability and robustness is especially important for high-risk fields such as cl…
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…