Machine Learning
Interpretable ML Methods, Data Modeling, Anomaly Detection
The Distinguished Research Group of Prof. Dr. Klaus-Robert Müller is concentrating on the development of robust and interpretable machine learning methods for learning from complex structured and non-stationary data and the fusion of heterogeneous multi-modal data sources. A special focus lies on the efficient modeling of non-stationary, heterogeneous and structured data sources with deep learning and kernel methods. He and his team also work on theoretically sound incorporation of a priori knowledge from the application domain as well as the detection of anomalies in structured data. The resulting models are expected not only to be accurate, but also to explain their nonlinear decisions, quantify decision uncertainties, and create new knowledge about the studied data. In addition, Klaus-Robert Müller has been pursuing a long history of bringing machine learning into the sciences, which has helped to arrive at genuinely novel insights. In the last decade his attention has focused primarily on quantum chemistry, cancer research as well as computational neuroscience.
xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell Lung Cancer
Dicke superradiant heat current enhancement in circuit quantum electrodynamics
Training objective drives the consistency of representational similarity across datasets
Researcher Spotlight: Dr. Lisa Raithel
Dr. Lisa Raithel is working at the integration of natural language processing (NLP) and medicine. Recently completing her PhD, she dives deep into the multilingual analysis of health data to uncover the real-world effects of medications.
Explainable AI illuminates the course of history
Understanding the evolution and dissemination of human knowledge over time is a long-cherished dream of many historians. A dream that faced many challenges due to the abundance of historical materials and limited specialist resources. However, the digitization of many historical archives presents new opportunities for AI-supported analysis.
AI in medicine: new approach for more efficient diagnostics
Researchers from LMU, BIFOLD, and Charité have developed a new AI tool that uses imaging data to also detect less frequent diseases of the gastrointestinal tract. In contrast to conventional models, the new AI only needs training data from common findings to detect deviations.
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