Kronecker Product Approximation

Kronecker product approximation is a technique used to efficiently approximate large matrices, particularly those arising in machine learning optimization problems, by representing them as the Kronecker product of smaller matrices. Current research focuses on applying this approximation within optimization algorithms like Shampoo and natural gradient methods, particularly in decentralized and Riemannian settings, improving computational efficiency and scalability. This approach is proving valuable in diverse applications, including accelerating training of deep neural networks and enhancing Bayesian model selection for improved generalization and data efficiency. The resulting speedups and improved performance are significant for handling the increasingly large datasets and complex models prevalent in modern machine learning.