Post Processing

Post-processing in various fields involves refining initial outputs from models or systems to improve accuracy, reliability, and fairness. Current research focuses on leveraging deep learning architectures, such as U-Nets and transformers, alongside statistical methods like quantile regression forests and scoring rule minimization, to achieve these objectives. Applications range from enhancing weather forecasts and improving the quality of audio and image data to mitigating bias in machine learning models and optimizing the performance of speech recognition systems. These advancements contribute to more accurate, reliable, and equitable outcomes across diverse scientific and practical domains.