Selective State Space Model

Selective State Space Models (SSMs) are a class of efficient sequence modeling architectures designed to overcome the computational limitations of transformer-based models, particularly for long sequences. Current research focuses on refining SSM architectures like Mamba, exploring their application across diverse domains (e.g., video processing, time series forecasting, and meteorological downscaling), and improving their explainability and robustness. The resulting advancements in efficient long-range dependency modeling have significant implications for various fields, offering improved performance and scalability in applications constrained by computational resources.