Bidirectional State Space Model

Bidirectional state space models (BSSMs) are a class of sequence models designed to efficiently process long sequences of data while maintaining high accuracy, addressing limitations of computationally expensive alternatives like Transformers. Current research focuses on applying BSSMs, often based on variations of the "Mamba" architecture, to diverse domains including audio and image processing, time series analysis (e.g., ECG, EEG), and hyperspectral imaging, demonstrating their effectiveness in tasks like classification and denoising. This approach offers significant advantages in computational efficiency and memory usage, making BSSMs a promising alternative for applications where processing speed and resource constraints are critical.