Selective State Space

Selective state space models (SSMs) are a class of neural network architectures designed to efficiently model long-range dependencies in sequential data, addressing the computational limitations of transformers. Current research focuses on refining SSM architectures, particularly variations of the "Mamba" model, to improve performance across diverse applications including speech recognition, image processing, and time series forecasting. This research is significant because SSMs offer a compelling alternative to transformers, providing comparable or superior accuracy with significantly reduced computational complexity and memory requirements, leading to more efficient and scalable solutions for various machine learning tasks.