SOURCE-LINKED INTELLIGENCE
Transferable Evidence Reconstruction for Longitudinal Glucose Representations
Long physiological recordings contain many routine measurements, while predictive information is often concentrated in rare events, sustained burden, and recurring temporal patterns. Masked autoencoding recovers measurements; contrastive learning aligns views. We study self-supervision that explicitly prioritizes structured signal evidence. We introduce transferable evidence reconstruction (TER), which constructs evidence from unlabeled recordings, fits a fresh low-capacity reader on one recording group, and requires that reader to recover the same evidence in another group without refitting.
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-23T14:38:38.000Z
First collected: 2026-09-24T08:22:30.429Z. This is not the publication date.