1.4k citations · 1.8k across the 17 of their papers we have counts for
20 papers
Revisiting Explainable AI through Model-Independent Concept Dictionaries
Thomas Schnake, Doreen Schöppenthau, Alexander Meyer +3
Modern applications of AI rely on increasingly complex models. Explainable AI (XAI) has emerged as a set of techniques aimed at improving model transparency. However, existing XAI…
OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA
Maaike Galama, Nina Kozar-Gillan, Christina Embacher +18
The tumor microenvironment (TME) plays a central role in cancer progression, treatment response, and patient outcomes, yet large-scale, consistent, and quantitative TME characteriz…
Investigating the Robustness of Subtask Distillation under Spurious Correlation
Pattarawat Chormai, Klaus-Robert Müller, Grégoire Montavon
Subtask distillation is an emerging paradigm in which compact, specialized models are extracted from large, general-purpose 'foundation models' for deployment in environments with…
Fast and Accurate Explanations of Distance-Based Classifiers by Uncovering Latent Explanatory Structures
Florian Bley, Jacob Kauffmann, Simon León Krug +2
Distance-based classifiers, such as k-nearest neighbors and support vector machines, continue to be a workhorse of machine learning, widely used in science and industry. In practic…
Towards Robust Foundation Models for Digital Pathology
Jonah Kömen, Edwin D. de Jong, Julius Hense +9
Biomedical Foundation Models (FMs) are rapidly transforming AI-enabled healthcare research and entering clinical validation. However, their susceptibility to learning non-biologica…
xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
Marvin Sextro, Gabriel Dernbach, Kai Standvoss +5
Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for…
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…
XpertAI: uncovering regression model strategies for sub-manifolds
Simon Letzgus, Klaus-Robert Müller, Grégoire Montavon
In recent years, Explainable AI (XAI) methods have facilitated profound validation and knowledge extraction from ML models. While extensively studied for classification, few XAI so…
Manipulating Feature Visualizations with Gradient Slingshots
Dilyara Bareeva, Marina M. -C. Höhne, Alexander Warnecke +5
Feature Visualization (FV) is a widely used technique for interpreting concepts learned by Deep Neural Networks (DNNs), which synthesizes input patterns that maximally activate a g…
An XAI framework for robust and transparent data-driven wind turbine power curve models
Simon Letzgus, Klaus-Robert Müller
Wind turbine power curve models translate ambient conditions into turbine power output. They are essential for energy yield prediction and turbine performance monitoring. In recent…
Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks
Lorenz Linhardt, Klaus-Robert Müller, Grégoire Montavon
Robustness has become an important consideration in deep learning. With the help of explainable AI, mismatches between an explained model's decision strategy and the user's domain…
Disentangled Explanations of Neural Network Predictions by Finding Relevant Subspaces
Pattarawat Chormai, Jan Herrmann, Klaus-Robert Müller +1
Explainable AI aims to overcome the black-box nature of complex ML models like neural networks by generating explanations for their predictions. Explanations often take the form of…
DORA: Exploring Outlier Representations in Deep Neural Networks
Kirill Bykov, Mayukh Deb, Dennis Grinwald +2
Deep Neural Networks (DNNs) excel at learning complex abstractions within their internal representations. However, the concepts they learn remain opaque, a problem that becomes par…
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…
Toward Explainable AI for Regression Models
Simon Letzgus, Patrick Wagner, Jonas Lederer +3
In addition to the impressive predictive power of machine learning (ML) models, more recently, explanation methods have emerged that enable an interpretation of complex non-linear…
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy
Christopher J. Anders, David Neumann, Wojciech Samek +2
Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood. With recent advances in Explainable Artificial Intelligence…
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark
Sören Becker, Johanna Vielhaben, Marcel Ackermann +3
Explainable Artificial Intelligence (XAI) is targeted at understanding how models perform feature selection and derive their classification decisions. This paper explores post-hoc…
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models
Jacob Kauffmann, Klaus-Robert Müller, Grégoire Montavon
A common machine learning task is to discriminate between normal and anomalous data points. In practice, it is not always sufficient to reach high accuracy at this task, one also w…
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition
Grégoire Montavon, Sebastian Bach, Alexander Binder +2
Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems, e.g., image classification, natural language processi…
Analyzing Classifiers: Fisher Vectors and Deep Neural Networks
Sebastian Bach, Alexander Binder, Grégoire Montavon +2
Fisher Vector classifiers and Deep Neural Networks (DNNs) are popular and successful algorithms for solving image classification problems. However, both are generally considered `b…