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Found 2 papers, 1 papers with code
Learning shared neural manifolds from multi-subject FMRI data
In order to understand the connection between stimuli of interest and brain activity, and analyze differences and commonalities between subjects, it becomes important to learn a meaningful embedding of the data that denoises, and reveals its intrinsic structure.
MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data
In particular, in EHR data, some variables are {\em missing not at random (MNAR)} but deliberately not collected and thus are a source of information.
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