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Found 4 papers, 2 papers with code
Federated Learning for Clinical Structured Data: A Benchmark Comparison of Engineering and Statistical Approaches
Our goal was to bridge the gap by presenting the first comprehensive comparison of FL frameworks from both engineering and statistical domains.
Generative Models for Anomaly Detection and Design-Space Dimensionality Reduction in Shape Optimization
Our work presents a novel approach to shape optimization, with the twofold objective to improve the efficiency of global optimization algorithms while promoting the generation of high-quality designs during the optimization process free of geometrical anomalies.
Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks
Providing a model that achieves a strong predictive performance and at the same time is interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives.
Recurrent-type Neural Networks for Real-time Short-term Prediction of Ship Motions in High Sea State
The prediction capability of recurrent-type neural networks is investigated for real-time short-term prediction (nowcasting) of ship motions in high sea state.
