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Found 5 papers, 2 papers with code
Towards a Personal Health Large Language Model
Here we present Personal Health Large Language Model (PH-LLM), fine-tuned from Gemini for understanding and reasoning over numerical time-series personal health data.
Multimodal LLMs for health grounded in individual-specific data
To effectively solve personalized health tasks, LLMs need the ability to ingest a diversity of data modalities that are relevant to an individual's health status.
Large-scale machine learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology
A GWAS of ML-based VCDR identified 299 independent genome-wide significant (GWS; $P\leq5\times10^{-8}$) hits in 156 loci.
The Search for Sparse, Robust Neural Networks
Recent work on deep neural network pruning has shown there exist sparse subnetworks that achieve equal or improved accuracy, training time, and loss using fewer network parameters when compared to their dense counterparts.
Generative Well-intentioned Networks
We propose Generative Well-intentioned Networks (GWINs), a novel framework for increasing the accuracy of certainty-based, closed-world classifiers.
