Data Optimization

Data optimization focuses on improving the quality and efficiency of data used in machine learning and deep learning applications, aiming to enhance model performance and robustness. Current research emphasizes automated feature engineering techniques, such as graph-based approaches that leverage historical data and enable dynamic backtracking for more efficient exploration of feature transformations, as well as optimizing data handling for large-scale deep learning models, including resource allocation and scheduling. These advancements are crucial for addressing challenges like data scarcity, improving model accuracy and generalizability, and enabling wider adoption of computationally intensive deep learning models across various scientific and practical domains.