arXiv:2605.21108v1 Announce Type: new Abstract: Latent state space systems are ubiquitous in statistical modelling, arising naturally when a time series is observed through a noisy measurement function, however training deep state space models (DSSM) at scale remains difficult. Two largely distinct strategies and literatures have developed around the training of DSSMs. Firstly, auto-encoding DSSMs train generative DSSMs by optimising a variational lower bound. Secondly, DSSMs trained by back-propagating the outputs of a classical sequential Monte Carlo algorithm (SMC). Such approaches can trai
The continuous push for more efficient and scalable AI models makes ongoing research into fundamental learning mechanisms for deep state space models timely and relevant.
Improved training methods for Deep State Space Models (DSSM) could lead to more accurate and robust AI systems across various applications, from time-series forecasting to complex control systems.
This research outlines a potentially more efficient method for training DSSMs, which could accelerate development and deployment of these sophisticated AI architectures.
- · AI researchers
- · Machine learning startups
- · Sectors using time-series data (e.g., finance, autonomous systems)
- · AI infrastructure providers
- · Developers relying on less efficient DSSM training methods
More widespread and effective use of Deep State Space Models in practical applications.
Acceleration of research and development in areas dependent on robust time-series analysis and generative models.
Potential for new AI services and products leveraging highly efficient learning for complex, dynamic systems.
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Read at arXiv cs.LG