New Wrapper Method

Wrapper methods are being actively developed to enhance the performance and usability of various machine learning models. Current research focuses on improving uncertainty estimation in Bayesian neural networks and deep ensembles, optimizing trajectory planning for autonomous vehicles, and enhancing the efficiency and interactivity of multi-modal AI systems, often through the integration of auxiliary networks or novel feature selection techniques. These advancements aim to bridge the gap between theoretical models and real-world applications, leading to more robust, efficient, and user-friendly AI systems across diverse domains.