Action-conditioned JEPA models treat pathology as a transition vector on latent states to simulate cardiac dynamics, outperforming supervised learning by over 0.05 AUROC in low-resource regimes on MIMIC-IV-ECG.
Multi-scale masked autoencoder for electrocardiogram anomaly detection.arXiv preprint arXiv:2502.05494
2 Pith papers cite this work. Polarity classification is still indexing.
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A compact 0.09B model using hierarchical discrete tokenization and prompted latent translation outperforms larger baselines in cross-modal PPG-to-ECG synthesis and cross-frequency super-resolution.
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Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs
Action-conditioned JEPA models treat pathology as a transition vector on latent states to simulate cardiac dynamics, outperforming supervised learning by over 0.05 AUROC in low-resource regimes on MIMIC-IV-ECG.
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Compact Latent Manifold Translation: A Parameter-Efficient Foundation Model for Cross-Modal and Cross-Frequency Physiological Signal Synthesis
A compact 0.09B model using hierarchical discrete tokenization and prompted latent translation outperforms larger baselines in cross-modal PPG-to-ECG synthesis and cross-frequency super-resolution.