Learning Global Additive Explanations for Neural Nets Using Model Distillation

ICLR 2019 Sarah TanRich CaruanaGiles HookerPaul KochAlbert Gordo

Interpretability has largely focused on local explanations, i.e. explaining why a model made a particular prediction for a sample. These explanations are appealing due to their simplicity and local fidelity... (read more)

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