ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
Tts-cgan: A transformer time-series conditional gan for biosignal data augmentation.arXiv preprint arXiv:2206.13676
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ECG-WM combines ODE physiological priors with latent diffusion models to generate intervention-conditioned ECG trajectories and uses diffusion stochasticity for uncertainty-aware clinical risk assessment.
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Exploring the Potential of Probabilistic Transformer for Time Series Modeling: A Report on the ST-PT Framework
ST-PT turns transformers into explicit factor graphs for time series, enabling structural injection of symbolic priors, per-sample conditional generation, and principled latent autoregressive forecasting via MFVI iterations.
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ECG-WM: A Physiology-Informed ECG World Model for Clinical Intervention Simulation
ECG-WM combines ODE physiological priors with latent diffusion models to generate intervention-conditioned ECG trajectories and uses diffusion stochasticity for uncertainty-aware clinical risk assessment.