Global Convolution

Global convolution, a technique for processing data with kernels that encompass the entire input, aims to efficiently capture long-range dependencies in sequential and spatial data, overcoming limitations of local convolutions. Current research focuses on developing efficient global convolution architectures, such as those based on state-space models, wavelet transforms, and reparameterizations, to address computational complexity issues and improve performance on tasks like long-sequence modeling, image classification, and graph learning. These advancements are significant because they enable the application of powerful global context modeling to large-scale datasets and resource-constrained environments, improving accuracy and efficiency in various fields.