  Modern machine learning systems based on neural networks have shown great
success in learning complex data patterns while being able to make good
predictions on unseen data points. However, the limited interpretability of
these systems hinders further progress and application to several domains in
the real world. This predicament is exemplified by time consuming model
selection and the difficulties faced in predictive explainability, especially
in the presence of adversarial examples. In this paper, we take a step towards
better understanding of neural networks by introducing a local polytope
interpolation method. The proposed Deep Non Negative Kernel regression (NNK)
interpolation framework is non parametric, theoretically simple and
geometrically intuitive. We demonstrate instance based explainability for deep
learning models and develop a method to identify models with good
generalization properties using leave one out estimation. Finally, we draw a
rationalization to adversarial and generative examples which are inevitable
from an interpolation view of machine learning.
