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Deep kernel principal component analysis for multi-level feature learning KU Leuven
Principal Component Analysis (PCA) and its nonlinear extension Kernel PCA (KPCA) are widely used across science and industry for data analysis and dimensionality reduction. Modern deep learning tools have achieved great empirical success, but a framework for deep principal component analysis is still lacking. Here we develop a deep kernel PCA methodology (DKPCA) to extract multiple levels of the most informative components of the data. Our ...
The “Eagle” Approach To Train Electrical Engineers With Collaborative Problem-Solving Skills KU Leuven
Engineering education plays a critical role in addressing the ever-increasing environmental and societal challenges, and collaborative problem solving (CPS) is a vital skill for engineers to tackle such complex multidisciplinary challenges and develop high-quality solutions. The EAGLE project at KU Leuven exemplifies CPS implementation in electrical engineering education, providing students with real-world connections and deep learning ...