104 citations · 298 across the 20 of their papers we have counts for
4 papers · 1 filter
Prediction-Constrained Topic Models for Antidepressant Recommendation
Michael C. Hughes, Gabriel Hope, Leah Weiner +4
Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks. We propose a framework for training supervis…
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
Mike Wu, Michael C. Hughes, Sonali Parbhoo +3
The lack of interpretability remains a key barrier to the adoption of deep models in many applications. In this work, we explicitly regularize deep models so human users might step…
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…
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
Andrew Slavin Ross, Michael C. Hughes, Finale Doshi-Velez
Neural networks are among the most accurate supervised learning methods in use today, but their opacity makes them difficult to trust in critical applications, especially when cond…