Deep State Space Model

Deep state-space models (DSSMs) are a class of neural networks designed for modeling sequential data, aiming to capture complex temporal dependencies and generate accurate predictions. Current research emphasizes efficient inference and training methods, particularly focusing on architectures like recurrent neural networks with specialized structures (e.g., diagonal forms) and the integration of attention mechanisms to improve robustness and long-range dependency modeling. DSSMs are proving valuable across diverse applications, including speech enhancement, time-series forecasting (e.g., cryptocurrency prices), and modeling complex dynamical systems like human movement and interacting agents, offering both improved performance and enhanced interpretability compared to traditional methods.