Confidence Based

Confidence-based methods are increasingly used to improve the reliability and robustness of various machine learning models. Current research focuses on developing and integrating confidence measures into diverse applications, including speech recognition, neural radiance fields, and robotic skill reproduction, often leveraging techniques like filtering low-confidence predictions or dynamically weighting model outputs based on estimated certainty. This work aims to enhance model performance, particularly in noisy or uncertain environments, and improve the safety and trustworthiness of AI systems in real-world deployments. The resulting advancements have significant implications for various fields, from autonomous driving to human-robot interaction.