AURORA is a representation learning framework that uses contextual orthogonalization and relational alignment to create disentangled, geometrically interpretable latent spaces in healthcare foundation models.
Yonglong Tian, Dilip Krishnan, and Phillip Isola
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
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.
WISTERIA learns robust clinical representations from noisy EHR labels by enforcing consistency across multiple weak supervision views plus ontology regularization.
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.
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WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records
WISTERIA learns robust clinical representations from noisy EHR labels by enforcing consistency across multiple weak supervision views plus ontology regularization.