Model Precondition

Model preconditioning aims to improve the efficiency and accuracy of various computational processes by modifying input data or model parameters to enhance performance. Current research focuses on developing data-efficient preconditioning methods for machine learning, particularly using techniques like decision tree learning and graph neural networks, as well as improving the conditioning of matrices in numerical linear algebra and optimization problems through normalization and Kronecker factorization. These advancements have significant implications for diverse fields, including robotics, deep learning, and the solution of large-scale scientific problems, by accelerating computation and improving the reliability of predictions and control systems.