LeNEPA proposes a no-augmentation next-latent prediction recipe that maintains frozen-probe performance across ECG and synthetic diagnostic time-series datasets under fixed-recipe conditions where a tuned JEPA baseline degrades.
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MERIT applies information theory to ECG representation learning via masked modeling and ECG-text contrastive alignment, reporting F1 gains over 3% on PTB-XL All and 5% on SubClass plus zero-shot and text generation improvements.
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LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning
LeNEPA proposes a no-augmentation next-latent prediction recipe that maintains frozen-probe performance across ECG and synthetic diagnostic time-series datasets under fixed-recipe conditions where a tuned JEPA baseline degrades.
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Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals
MERIT applies information theory to ECG representation learning via masked modeling and ECG-text contrastive alignment, reporting F1 gains over 3% on PTB-XL All and 5% on SubClass plus zero-shot and text generation improvements.