We propose a method for modeling dynamical systems, that bridges efficient latent space modeling with entity tracking by introducing identifier representations that maintain entity traceability within a latent system representation.

We propose OmniCast, a scalable and skillful probabilistic model that unifies weather forecasting across timescales.

This paper proposes LD3M, a novel framework for latent dataset distillation with generative priors that improves the gradient flow of diffusion models during the distillation process.

We propose a robust and general image fusion framework that requires no additional training, while effectively adapting to diverse fusion scenarios and effectively addressing various forms of interference.