Diffusion-Driven State Space Models
In many domains, practitioners seek models that produce accurate forecasts while faithfully capturing latent system dynamics. Existing approaches typically sacrifice one of these goals: deep state space models often assume Gaussian latent transitions, limiting fit and forecasting, while diffusion models are highly expressive but lack principled inference for the underlying dynamics. To combine the strengths of both, we introduce the Diffusion-Driven State Space Model (DDSSM), which replaces the conventional Gaussian transition distribution with a diffusion model. Our DDSSM resolves the open problem of how to jointly train an autoencoder and a diffusion model on sequential data, thereby ex...
| Authors | Jack Ruder, Michael Wojnowicz |
|---|---|
| Primary category | stat.ML |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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