Continuous Convolution

Continuous convolution extends traditional discrete convolutions to handle irregularly sampled data and continuous functions, aiming to improve efficiency and expressiveness in various applications. Current research focuses on developing efficient algorithms for continuous convolution, including novel neural network architectures like those based on integral and differential operators, and exploring its application in diverse fields such as PDE solving, image processing, and event sequence modeling. These advancements offer the potential for improved performance in deep learning models, more accurate scientific simulations, and enhanced signal processing capabilities for continuous data representations.