10 citations · 24 across the 8 of their papers we have counts for
12 papers
Decoupling Time and Space: A Temporally Conditioned Refinement for EEG Source Imaging
Marco Morik, Jesse Palarus, Carmen Vidaurre +2
Electroencephalography (EEG) offers millisecond temporal resolution, but inferring underlying neural sources is a severely ill-posed spatial inverse problem. While deep learning ha…
Towards Fast and Disentangled Counterfactuals for Visual Foundation Models
Sidney Bender, Benedikt Kunz, Ahmed Zeid +3
Foundation models remain vulnerable to spurious correlations and ``Clever Hans'' strategies. Explainable machine learning can find and remove such strategies for classifiers withou…
Explaining Hyperbolic Neural Networks via Geometry-Aware Relevance Propagation
Ping Xiong, Shanglin Li, Yi Ding +2
Hyperbolic neural networks introduce geometric operations that require explicit treatment in relevance propagation. Equivalent geometric realizations can produce different feature…
Explaining Temporal Graph Neural Networks via Feature-induced Information Flow
Ping Xiong, Thomas Schnake, Klaus-Robert Müller +1
Event-based Temporal Graph Neural Networks (ETGNNs) have demonstrated strong performance across a wide range of applications, including social network analysis, epidemic tracing, r…
Normalized Relevance Measure as a Unifying Framework to Explain Neural Network Latent Structures
Ping Xiong, Thomas Schnake, Grégoire Montavon +2
To understand how a neural network (NN) functions and makes predictions, it has become increasingly clear that analyzing only the input domain is insufficient -- one must also exam…
Uncovering the Structure of Explanation Quality with Spectral Analysis
Johannes Maeß, Grégoire Montavon, Shinichi Nakajima +2
As machine learning models are increasingly considered for high-stakes domains, effective explanation methods are crucial to ensure that their prediction strategies are transparent…
Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models
Khaled Kahouli, Winfried Ripken, Stefan Gugler +3
Diffusion models have been widely adopted as a powerful generative framework across numerous scientific domains, yet their inherently iterative sampling process remains a significa…
Enhancing Brain Source Reconstruction by Initializing 3D Neural Networks with Physical Inverse Solutions
Marco Morik, Ali Hashemi, Klaus-Robert Müller +2
Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high…
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
Thomas Schnake, Farnoush Rezaei Jafari, Jonas Lederer +5
Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency and trust in AI systems, where traditional XAI approaches typically offer one level of abst…
Molecular relaxation by reverse diffusion with time step prediction
Khaled Kahouli, Stefaan Simon Pierre Hessmann, Klaus-Robert Müller +3
Molecular relaxation, finding the equilibrium state of a non-equilibrium structure, is an essential component of computational chemistry to understand reactivity. Classical force f…
Self-Supervised Training with Autoencoders for Visual Anomaly Detection
Alexander Bauer, Shinichi Nakajima, Klaus-Robert Müller
We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. Here, regularized autoencoders provide a…
Automatic Identification of Types of Alterations in Historical Manuscripts
David Lassner, Anne Baillot, Sergej Dogadov +2
Alterations in historical manuscripts such as letters represent a promising field of research. On the one hand, they help understand the construction of text. On the other hand, to…