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Found 2 papers, 1 papers with code
Diffusion models as probabilistic neural operators for recovering unobserved states of dynamical systems
In this work, we show that diffusion-based generative models exhibit many properties favourable for neural operators, and they can effectively generate the solution of a PDE conditionally on the parameter or recover the unobserved parts of the system.
Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series
Experiments on challenging real-world epidemiological and semi-synthetic data demonstrate the advantages of the M-CHMM: improved data fit, capacity to efficiently handle missing and noisy measurements, improved prediction accuracy, and ability to identify interpretable subsets in the data.
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