Numerical Representation

Numerical representation focuses on converting various data types—including strings, images, and complex model outputs—into numerical formats suitable for machine learning and computational analysis. Current research emphasizes developing efficient and robust methods for this conversion, exploring techniques like reinforcement learning for quantitative difficulty assessment, polynomial radix-2 indexing for parallel computation, and semantic embeddings from large language models for multimodal data. These advancements are crucial for improving the performance and applicability of machine learning algorithms across diverse scientific domains and practical applications, such as autonomous driving and materials science.