HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.
CLIP-KD: An empirical study of distilling CLIP models
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Multi-teacher confidence-weighted ensembling in unsupervised prompt distillation raises average harmonic mean from 87.52 to 89.28 across four base-to-novel datasets, with largest gains on domain-shifted EuroSAT.
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The Professor: Multi-Teacher Unsupervised Prompt Distillation for Vision-Language Models
Multi-teacher confidence-weighted ensembling in unsupervised prompt distillation raises average harmonic mean from 87.52 to 89.28 across four base-to-novel datasets, with largest gains on domain-shifted EuroSAT.