SPIDeC methods achieve arbitrarily high-order accuracy for positive dynamical systems while unconditionally preserving positivity and equilibria via a multiplicative Volterra structure, and they are L-stable with asymptotic logarithmic contractivity under Gauss-Radau nodes.
A transformer- based unified multimodal framework for Alzheimer’s disease assess- ment
2 Pith papers cite this work. Polarity classification is still indexing.
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A Temporal Fusion Transformer with CORAL ordinal layer and autoregressive Mixture Density Network generates multi-horizon probabilistic trajectories and decomposed uncertainty estimates for Alzheimer's progression on ADNI data.
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Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning
A Temporal Fusion Transformer with CORAL ordinal layer and autoregressive Mixture Density Network generates multi-horizon probabilistic trajectories and decomposed uncertainty estimates for Alzheimer's progression on ADNI data.