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DeSBi
DeSBi

KI-FOR 5363 DeSBi

Fusing Deep Learning and Statistics towards Understanding Structured Biomedical Data (DeSBi)

Publications

All Publications

Paulo Yanez, Simon Witzke, Nadja Klein, Bernhard Y. Renard: Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation

Published in: Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2024. Lecture Notes in Computer Science

Abstract:
Explainability is a key component in many applications involving deep neural networks (DNNs). However, current explanation methods for DNNs commonly leave it to the human observer to distinguish relevant explanations from spurious noise. This is not feasible anymore when going from easily human-accessible data such as images to more complex data such as genome...

Marco Simnacher, Xiangnan Xu, Hani Park, Christoph Lippert, Sonja Greven: Deep Nonparametric Conditional Independence Tests for Images

Published in: arXiv

Abstract:
Conditional independence tests (CITs) test for conditional dependence between random variables. As existing CITs are limited in their applicability to complex, high-dimensional variables such as images, we introduce deep nonparametric CITs (DNCITs). The DNCITs combine embedding maps, which extract feature representations of high-dimensional variables, with nonparametric CITs applicable to these feature representations.