Uncertainty Estimation Method

Uncertainty estimation methods aim to quantify the confidence of machine learning models' predictions, crucial for deploying AI in high-stakes applications where reliability is paramount. Current research focuses on improving the accuracy and efficiency of these methods, exploring techniques like deep ensembles, Bayesian neural networks, and evidential deep learning, often tailored to specific model architectures and tasks (e.g., image segmentation, natural language processing). This field is vital for building trustworthy AI systems, addressing concerns about model robustness, out-of-distribution detection, and enabling more reliable decision-making in diverse domains like healthcare and autonomous driving.