Konrad Rieck — accepted AI-conference papers

2025

Adversarial Inputs for Linear Algebra Backends

Jonas Möller, Lukas Pirch, Felix Weissberg, Sebastian Baunsgaard, Thorsten Eisenhofer, Konrad Rieck

ICML 2025poster

Linear algebra is a cornerstone of neural network inference. The efficiency of popular frameworks, such as TensorFlow and PyTorch, critically depends on backend libraries providing highly optimized matrix multiplications and convolutions. A diverse range of these backends exists across platforms, in…

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2025

Manipulating Feature Visualizations with Gradient Slingshots

Dilyara Bareeva, Marina MC Höhne, Alexander Warnecke, Lukas Pirch, Klaus Robert Muller, Konrad Rieck +2

NeurIPS 2025poster

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 given feature. Despite its popularity, the trustworthiness of FV explanations has received limited attention. We introduc…

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