164 citations · 180 across the 4 of their papers we have counts for
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stat.ML2017★ 4 cited
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models
Michael C. Hughes, Leah Weiner, Gabriel Hope +4
Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful. We propose a fra…
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…