Unified Model

Unified models in machine learning aim to consolidate multiple related tasks or modalities into a single framework, improving efficiency and performance compared to separate models. Current research focuses on developing unified architectures for diverse applications, including natural language processing (e.g., handling morphologically rich languages, multi-task learning), computer vision (e.g., audio-visual representation and generation, 3D object tracking), and other domains like traffic conflict detection and molecular inverse folding. These advancements offer significant potential for improving the scalability, robustness, and interpretability of machine learning systems across various scientific and practical applications.