Fast Kernel

Fast kernel methods aim to overcome the computational limitations of traditional kernel methods, which often suffer from high computational complexity, particularly with large datasets. Current research focuses on developing efficient algorithms, including sparsified sketching techniques and novel approaches like "top-tuning" for transfer learning, to accelerate kernel computations in various applications such as data quality monitoring, anomaly detection, and image classification. These advancements significantly improve the scalability and practicality of kernel methods, enabling their application to larger datasets and more complex problems across diverse scientific domains.