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08/18/2023
From Hope to Safety: Unlearning Biases of Deep Models by Enforcing the Right Reasons in Latent Space
Deep Neural Networks are prone to learning spurious correlations embedde...
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02/27/2023
Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories
We study the consequences of mode-collapse of normalizing flows in the c...
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02/07/2022
PatClArC: Using Pattern Concept Activation Vectors for Noise-Robust Model Debugging
State-of-the-art machine learning models are commonly (pre-)trained on l...
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06/24/2021
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy
Deep Neural Networks (DNNs) are known to be strong predictors, but their...
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12/18/2020
Towards Robust Explanations for Deep Neural Networks
Explanation methods shed light on the decision process of black-box clas...
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07/20/2020
Fairwashing Explanations with Off-Manifold Detergent
Explanation methods promise to make black-box classifiers more transpare...
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07/14/2020
On Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models
In this work, we demonstrate that applying deep generative machine learn...
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03/17/2020
Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond
With the broader and highly successful usage of machine learning in indu...
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12/22/2019
Analyzing ImageNet with Spectral Relevance Analysis: Towards ImageNet un-Hans'ed
Today's machine learning models for computer vision are typically traine...
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06/19/2019