Global Mixing

Global mixing, encompassing the blending of information across different data points or channels, is a central theme in diverse fields, aiming to improve model performance and understanding of complex systems. Current research focuses on developing sophisticated mixing strategies within various model architectures, including neural networks (e.g., MLP-Mixers, Transformers), and employing these strategies for tasks such as time series forecasting, image classification, and domain adaptation. These advancements are significant because effective global mixing enhances model robustness, generalizability, and efficiency in handling high-dimensional data and non-independent samples, impacting fields ranging from machine learning to neuroscience.