Klaus‐Robert Müller
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View article: Dynamic presentation in 3D modulates face similarity judgments – A human-aligned encoding model approach
Dynamic presentation in 3D modulates face similarity judgments – A human-aligned encoding model approach Open
Face perception dynamically unfolds in three-dimensional space, yet, experimental paradigms predominantly rely on static 2D images, limiting insights into real-world face processing. We conducted a pre-registered study comparing face simil…
View article: Towards Robust Foundation Models for Digital Pathology
Towards Robust Foundation Models for Digital Pathology Open
Biomedical Foundation Models (FMs) are rapidly transforming AI-enabled healthcare research and entering clinical validation. However, their susceptibility to learning non-biological technical features -- including variations in surgical/en…
View article: Dynamic presentation in 3D modulates face similarity judgments – A human-aligned encoding model approach
Dynamic presentation in 3D modulates face similarity judgments – A human-aligned encoding model approach Open
Face perception dynamically unfolds in three-dimensional space, yet, experimental paradigms predominantly rely on static 2D images, limiting insights into real-world face processing. We conducted a pre-registered study comparing face simil…
View article: Sampling 3D Molecular Conformers with Diffusion Transformers
Sampling 3D Molecular Conformers with Diffusion Transformers Open
Diffusion Transformers (DiTs) have demonstrated strong performance in generative modeling, particularly in image synthesis, making them a compelling choice for molecular conformer generation. However, applying DiTs to molecules introduces …
View article: Towards Desiderata-Driven Design of Visual Counterfactual Explainers
Towards Desiderata-Driven Design of Visual Counterfactual Explainers Open
Visual counterfactual explainers (VCEs) are a straightforward and promising approach to enhancing the transparency of image classifiers. VCEs complement other types of explanations, such as feature attribution, by revealing the specific da…
View article: Dynamic presentation in 3D modulates face similarity judgments – A human-aligned encoding model approach
Dynamic presentation in 3D modulates face similarity judgments – A human-aligned encoding model approach Open
Face perception dynamically unfolds in three-dimensional space, yet, experimental paradigms predominantly rely on static 2D images, limiting insights into real-world face processing. We conducted a pre-registered study comparing face simil…
View article: How simple can you go? An off-the-shelf transformer approach to molecular dynamics
How simple can you go? An off-the-shelf transformer approach to molecular dynamics Open
Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures. This is in contrast to most other machine learning domains, where specialist approaches are inc…
View article: Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models
Disentangling Total-Variance and Signal-to-Noise-Ratio Improves Diffusion Models Open
The long sampling time of diffusion models remains a significant bottleneck, which can be mitigated by reducing the number of diffusion time steps. However, the quality of samples with fewer steps is highly dependent on the noise schedule,…
View article: Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields Open
Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces. A key ste…
View article: Parameterization of intraoperative human microelectrode recordings: Linking action potential morphology to brain anatomy
Parameterization of intraoperative human microelectrode recordings: Linking action potential morphology to brain anatomy Open
Deep brain stimulation (DBS) is a targeted manipulation of brain circuitry to treat neurological and neuropsychiatric conditions. Optimal DBS lead placement is essential for treatment efficacy. Current targeting practice is based on preope…
View article: Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics
Atlas: A Novel Pathology Foundation Model by Mayo Clinic, Charité, and Aignostics Open
Recent advances in digital pathology have demonstrated the effectiveness of foundation models across diverse applications. In this report, we present Atlas, a novel vision foundation model based on the RudolfV approach. Our model was train…
View article: Self-Supervised Autoencoders for Visual Anomaly Detection
Self-Supervised Autoencoders for Visual Anomaly Detection Open
We focus on detecting anomalies in images where the data distribution is supported by a lower-dimensional embedded manifold. Approaches based on autoencoders have aimed to control their capacity either by reducing the size of the bottlenec…
View article: Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost
Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost Open
Long-range correlations are essential across numerous machine learning tasks, especially for data embedded in Euclidean space, where the relative positions and orientations of distant components are often critical for accurate predictions.…
View article: Enhancing Brain Source Reconstruction through Physics-Informed 3D Neural Networks
Enhancing Brain Source Reconstruction through Physics-Informed 3D Neural Networks Open
Reconstructing brain sources is a fundamental challenge in neuroscience, crucial for understanding brain function and dysfunction. Electroencephalography (EEG) signals have a high temporal resolution. However, identifying the correct spati…
View article: Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
Analyzing Atomic Interactions in Molecules as Learned by Neural Networks Open
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a test set alone is not a guarantee for robust chemical modelin…
View article: Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields Open
Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces. A key ste…
View article: DNA Methylation Profiling of Salivary Gland Tumors Supports and Expands Conventional Classification
DNA Methylation Profiling of Salivary Gland Tumors Supports and Expands Conventional Classification Open
Tumors of the major and minor salivary glands histologically encompass a diverse and partly overlapping spectrum of frequent diagnostically challenging neoplasms. Despite recent advances in molecular testing and the identification of tumor…
View article: Human-aligned deep and sparse encoding models of dynamic 3D face similarity perception
Human-aligned deep and sparse encoding models of dynamic 3D face similarity perception Open
Face perception happens dynamically over time and primarily in three-dimensional space. Perceived similarity, including identity, should ideally remain invariant to changes along these dimensions. Surprisingly, much of our knowledge about …
View article: Dynamic presentation in 3D modulates face similarity judgments – A human-aligned encoding model approach
Dynamic presentation in 3D modulates face similarity judgments – A human-aligned encoding model approach Open
Face perception dynamically unfolds in three-dimensional space, yet, experimental paradigms predominantly rely on static 2D images, limiting insights into real-world face processing. We conducted a pre-registered study comparing face simil…
View article: Aligning Machine and Human Visual Representations across Abstraction Levels
Aligning Machine and Human Visual Representations across Abstraction Levels Open
Deep neural networks have achieved success across a wide range of applications, including as models of human behavior and neural representations in vision tasks. However, neural network training and human learning differ in fundamental way…
View article: Modeling Attention and Binding in the Brain through Bidirectional Recurrent Gating
Modeling Attention and Binding in the Brain through Bidirectional Recurrent Gating Open
Attention is a key component of the visual system, essential for perception, learning, and memory. Attention can also be seen as a solution to the binding problem: concurrent attention to all parts of an entity allows separating it from th…
View article: Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties
Complete and Efficient Covariants for 3D Point Configurations with Application to Learning Molecular Quantum Properties Open
When modeling physical properties of molecules with machine learning, it is desirable to incorporate $SO(3)$-covariance. While such models based on low body order features are not complete, we formulate and prove general completeness prope…
View article: Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features Open
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 abstraction for explanations, often in the form of heatmaps hig…
View article: Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study
Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study Open
Although deep learning models trained on larger cohorts show improved robustness and generalizability in predicting oncogenic mutations, they cannot replace comprehensive molecular profiling. However, they may support patient pre-selection…
View article: Molecular relaxation by reverse diffusion with time step prediction
Molecular relaxation by reverse diffusion with time step prediction Open
Molecular relaxation, finding the equilibrium state of a non-equilibrium structure, is an essential component of computational chemistry to understand reactivity. Classical force field (FF) methods often rely on insufficient local energy m…
View article: A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery
A Machine Learning and Explainable AI Framework Tailored for Unbalanced Experimental Catalyst Discovery Open
The successful application of machine learning (ML) in catalyst design relies on high-quality and diverse data to ensure effective generalization to novel compositions, thereby aiding in catalyst discovery. However, due to complex interact…