Author

R. Rehms

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#artificial intelligence Preprint Sep 2026

NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting

Noah is a time-aware, task-agnostic, generative transformer model representing and forecasting the full multimodal patient journey, and is the first truly holistic generative model in its field, enabling autoregressive forecasting with optional time control, zero-shot classification, and counterfactual intervention sim...

Tobias Susetzky, R. Rehms, Dmitrii Seletkov et al. · 0 citations

Aug 2026

It depends: Incorporating correlations for joint aleatoric and epistemic uncertainties of high-dimensional output spaces

This paper proposes a novel approach that approximates the resulting joint uncertainty using a low-rank plus diagonal covariance structure, capturing essential output correlations while avoiding the computational burdens of full covariance matrices.

Leonhard F. Feiner, M. Nickel, M. Menten et al. · Trans. Mach. Learn. Res. · 0 citations