1D Kernel

One-dimensional (1D) kernels are increasingly used in various machine learning applications, primarily aiming to improve efficiency and performance in tasks involving sequential or structured data, such as time series forecasting and image classification. Research focuses on developing novel 1D kernel parameterizations, including multi-resolution and oriented approaches, often within convolutional neural networks (CNNs) or integrated with transformer architectures. These advancements offer significant potential for reducing computational complexity while maintaining or improving accuracy in diverse fields, from remote sensing to acoustic scene classification and graph-based learning.