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MambaLRP: Explaining Selective State Space Sequence Models
Farnoush Rezaei Jafari, Grégoire Montavon, Klaus-Robert Müller +1
Recent sequence modeling approaches using selective state space sequence models, referred to as Mamba models, have seen a surge of interest. These models allow efficient processing…
Diffeomorphic Counterfactuals with Generative Models
Ann-Kathrin Dombrowski, Jan E. Gerken, Klaus-Robert Müller +1
Counterfactuals can explain classification decisions of neural networks in a human interpretable way. We propose a simple but effective method to generate such counterfactuals. Mor…
Fairwashing Explanations with Off-Manifold Detergent
Christopher J. Anders, Plamen Pasliev, Ann-Kathrin Dombrowski +2
Explanation methods promise to make black-box classifiers more transparent. As a result, it is hoped that they can act as proof for a sensible, fair and trustworthy decision-making…
Automatic Identification of Types of Alterations in Historical Manuscripts
David Lassner, Anne Baillot, Sergej Dogadov +2
Alterations in historical manuscripts such as letters represent a promising field of research. On the one hand, they help understand the construction of text. On the other hand, to…
Forecasting Industrial Aging Processes with Machine Learning Methods
Mihail Bogojeski, Simeon Sauer, Franziska Horn +1
Accurately predicting industrial aging processes makes it possible to schedule maintenance events further in advance, ensuring a cost-efficient and reliable operation of the plant.…
Pruning by Explaining: A Novel Criterion for Deep Neural Network Pruning
Seul-Ki Yeom, Philipp Seegerer, Sebastian Lapuschkin +4
The success of convolutional neural networks (CNNs) in various applications is accompanied by a significant increase in computation and parameter storage costs. Recent efforts to r…