Tree regularization, which penalizes the decision path length of a tree fitted to a deep network's predictions, produces deep models with higher accuracy at low complexity than L1 or L2 penalties.
K., Rathod, V., Murphy, K
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Optimizing for Interpretability in Deep Neural Networks with Tree Regularization
Tree regularization, which penalizes the decision path length of a tree fitted to a deep network's predictions, produces deep models with higher accuracy at low complexity than L1 or L2 penalties.