Simple Alternating Mixer

Simple alternating mixers are a class of neural network architectures designed to efficiently and effectively combine information from different sources or perspectives within data. Current research focuses on developing and applying these mixers in various domains, including sequence modeling (e.g., using Monarch and Hydra mixers), time series forecasting (e.g., with IIP-Mixer and MTS-Mixers), and image processing (e.g., CS-Mixer), often replacing or augmenting attention mechanisms. This approach offers advantages in terms of computational efficiency and scalability, leading to improved performance in diverse applications such as video prediction, music mixing, and medical image analysis.