2.8k citations · 4k across the 11 of their papers we have counts for
22 papers
Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations
Alexander Binder, Leander Weber, Sebastian Lapuschkin +3
While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifical…
Deep learning for surrogate modelling of 2D mantle convection
Siddhant Agarwal, Nicola Tosi, Pan Kessel +2
Traditionally, 1D models based on scaling laws have been used to parameterized convective heat transfer rocks in the interior of terrestrial planets like Earth, Mars, Mercury and V…
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen +5
Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text.…
GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis
Kateryna Melnyk, Stefan Klus, Grégoire Montavon +1
More and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern h…
The Clever Hans Effect in Anomaly Detection
Jacob Kauffmann, Lukas Ruff, Grégoire Montavon +1
The 'Clever Hans' effect occurs when the learned model produces correct predictions based on the 'wrong' features. This effect which undermines the generalization capability of an…
Building and Interpreting Deep Similarity Models
Oliver Eberle, Jochen Büttner, Florian Kräutli +3
Many learning algorithms such as kernel machines, nearest neighbors, clustering, or anomaly detection, are based on the concept of 'distance' or 'similarity'. Before similarities a…