104 citations · 298 across the 20 of their papers we have counts for
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stat.ML2016★ 7 cited
Supervised topic models for clinical interpretability
Michael C. Hughes, Huseyin Melih Elibol, Thomas McCoy +2
Supervised topic models can help clinical researchers find interpretable cooccurence patterns in count data that are relevant for diagnostics. However, standard formulations of sup…
stat.ML2016★ 6 cited
Fast Learning of Clusters and Topics via Sparse Posteriors
Michael C. Hughes, Erik B. Sudderth
Mixture models and topic models generate each observation from a single cluster, but standard variational posteriors for each observation assign positive probability to all possibl…