Unified Training

Unified training aims to improve the efficiency and effectiveness of training machine learning models by integrating different training stages or paradigms into a single process. Current research focuses on applying this approach to diverse areas, including time series forecasting (using Transformer architectures), stereo matching, and solving partial differential equations (employing novel interpolation-based networks). This approach promises to accelerate model development, reduce computational costs, and enhance performance across various applications by leveraging synergies between different training methods and data sources.