104 citations · 298 across the 20 of their papers we have counts for
Showing 2019Show all
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cs.LG2019★ 8 cited
Optimizing for Interpretability in Deep Neural Networks with Tree Regularization
Mike Wu, Sonali Parbhoo, Michael C. Hughes +2
Deep models have advanced prediction in many domains, but their lack of interpretability remains a key barrier to the adoption in many real world applications. There exists a large…
cs.LG2019★ 8 cited
Regional Tree Regularization for Interpretability in Black Box Models
Mike Wu, Sonali Parbhoo, Michael Hughes +5
The lack of interpretability remains a barrier to the adoption of deep neural networks. Recently, tree regularization has been proposed to encourage deep neural networks to resembl…