Contrastive predictive coding pretraining combined with structured state space models yields the strongest ECG foundation models, with continued gains from scaling data to 11 million samples.
Self-supervised learning from images with a joint-embedding predictive architecture
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A new diagnostic framework using inpainted context ratios and laterality checks on a Pantanal jaguar benchmark reveals whether re-ID models depend on coat patterns or spurious background evidence.
CortexMAE adapts Vision Transformers to fMRI via cortical flat maps, shows power-law scaling on 2.1K hours of data, and outperforms priors on cognitive state decoding while failing to beat a simple functional connectivity baseline on subject-level trait prediction.
sREPA enforces structural consistency in relational geometry of pre-trained vision features to accelerate DiT training and improve generation quality.
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.
citing papers explorer
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Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study
Contrastive predictive coding pretraining combined with structured state space models yields the strongest ECG foundation models, with continued gains from scaling data to 11 million samples.
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Are We Recognizing the Jaguar or Its Background? A Diagnostic Framework for Jaguar Re-Identification
A new diagnostic framework using inpainted context ratios and laterality checks on a Pantanal jaguar benchmark reveals whether re-ID models depend on coat patterns or spurious background evidence.
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Scaling Vision Transformers for Functional MRI with Flat Maps
CortexMAE adapts Vision Transformers to fMRI via cortical flat maps, shows power-law scaling on 2.1K hours of data, and outperforms priors on cognitive state decoding while failing to beat a simple functional connectivity baseline on subject-level trait prediction.
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Beyond Point-Wise Matching: Structural Representation Alignment for Accelerating Diffusion Transformers
sREPA enforces structural consistency in relational geometry of pre-trained vision features to accelerate DiT training and improve generation quality.
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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.