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Modern machine learning systems based on neural networks have shown greatsuccess in learning complex data patterns while being able to make goodpredictions on unseen data points. However, the limited interpretability ofthese systems hinders further progress and application to several domains inthe real world. This predicament is exemplified by time consuming modelselection and the difficulties faced in predictive explainability, especiallyin the presence of adversarial examples. In this paper, we take a step towardsbetter understanding of neural networks by introducing a local polytopeinterpolation method. The proposed Deep Non Negative Kernel regression (NNK)interpolation framework is non parametric, theoretically simple andgeometrically intuitive. We demonstrate instance based explainability for deeplearning models and develop a method to identify models with goodgeneralization properties using leave one out estimation. Finally, we draw arationalization to adversarial and generative examples which are inevitablefrom an interpolation view of machine learning.

DeepNNK: Explaining deep models and their generalization using polytope interpolation