Constraining neural network partial dependence to match domain-knowledge functional forms during training improves predictive accuracy, data efficiency, and explanation faithfulness on regression problems.
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Steering Neural Network Training through Interpretable Constraints Based on Partial Dependence
Constraining neural network partial dependence to match domain-knowledge functional forms during training improves predictive accuracy, data efficiency, and explanation faithfulness on regression problems.