CGM-JEPA learns transferable CGM representations via predictive self-supervised pretraining on unlabeled time series and cross-view distributional objectives, outperforming baselines on AUROC for insulin resistance and beta-cell dysfunction across modality shifts and cohorts.
Architecture and tokenizer dimensions are read from the artifact metadata, so the loader can reconstruct the model without recourse to a separate checkpoint config file
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CGM-JEPA: Learning Consistent Continuous Glucose Monitor Representations via Predictive Self-Supervised Pretraining
CGM-JEPA learns transferable CGM representations via predictive self-supervised pretraining on unlabeled time series and cross-view distributional objectives, outperforming baselines on AUROC for insulin resistance and beta-cell dysfunction across modality shifts and cohorts.