268 citations · 376 across the 6 of their papers we have counts for
6 papers
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…
xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology
Julius Hense, Mina Jamshidi Idaji, Oliver Eberle +7
Multiple instance learning (MIL) is an effective and widely used approach for weakly supervised machine learning. In histopathology, MIL models have achieved remarkable success in…
Insightful analysis of historical sources at scales beyond human capabilities using unsupervised Machine Learning and XAI
Oliver Eberle, Jochen Büttner, Hassan El-Hajj +3
Historical materials are abundant. Yet, piecing together how human knowledge has evolved and spread both diachronically and synchronically remains a challenge that can so far only…
XAI for Transformers: Better Explanations through Conservative Propagation
Ameen Ali, Thomas Schnake, Oliver Eberle +3
Transformers have become an important workhorse of machine learning, with numerous applications. This necessitates the development of reliable methods for increasing their transpar…
Higher-Order Explanations of Graph Neural Networks via Relevant Walks
Thomas Schnake, Oliver Eberle, Jonas Lederer +4
Graph Neural Networks (GNNs) are a popular approach for predicting graph structured data. As GNNs tightly entangle the input graph into the neural network structure, common explain…
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…