AURORA is a representation learning framework that uses contextual orthogonalization and relational alignment to create disentangled, geometrically interpretable latent spaces in healthcare foundation models.
Can large language models abstract medical coded language? arXiv preprint arXiv:2403.10822
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Event-centric waveform foundation models are learned via self-supervised consistency on latent event structures and interactions, yielding improved performance and label efficiency over sequence-based baselines on physiological tasks.
citing papers explorer
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AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models
AURORA is a representation learning framework that uses contextual orthogonalization and relational alignment to create disentangled, geometrically interpretable latent spaces in healthcare foundation models.
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Event Fields: Learning Latent Event Structure for Waveform Foundation Models
Event-centric waveform foundation models are learned via self-supervised consistency on latent event structures and interactions, yielding improved performance and label efficiency over sequence-based baselines on physiological tasks.