Data Encoding

Data encoding, the process of transforming data into a suitable format for machine learning, is a crucial area of research aiming to improve model performance and efficiency. Current efforts focus on developing novel encoding schemes for various data types, including images, time series, and text, often leveraging transformer architectures and autoregressive models to capture complex relationships and improve controllability. These advancements are significant because effective encoding directly impacts the accuracy, efficiency, and fairness of machine learning models across diverse applications, from medical image analysis to natural language processing. Furthermore, research is exploring the interplay between encoding and model interpretability, aiming to create more transparent and trustworthy AI systems.