Learned Kernel

Learned kernels are adaptable functions used in machine learning models to improve performance and interpretability by learning optimal similarity measures between data points. Current research focuses on developing efficient algorithms for training these kernels, exploring their application in diverse areas like image processing, time series analysis, and 3D reconstruction, and investigating their use within various architectures such as neural networks and support vector machines. This research is significant because learned kernels offer the potential to enhance model accuracy, reduce computational costs, and improve the explainability of complex machine learning models across a wide range of applications.