Non Local

Non-local methods in machine learning aim to capture long-range dependencies within data, overcoming limitations of locally-focused approaches like convolutions. Current research focuses on improving the efficiency and scalability of non-local operations, particularly through novel architectures like attention mechanisms and hybrid quantum-classical models, as well as integrating them with existing deep learning frameworks for tasks such as image processing, video analysis, and molecular modeling. These advancements are significant because they enhance the accuracy and performance of various applications, from image denoising and super-resolution to more complex problems in scientific computing and robotics.