# DeepNNK: Explaining deep models and their generalization using polytope interpolation

arXiv: https://arxiv.org/abs/2007.10505 · HTML: https://mlsanity.com/paper/2007.10505/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [Channel-Wise Early Stopping without a Validation Set via NNK Polytope Interpolation](https://mlsanity.com/paper/2107.12972/) (2021)
- [Generalization Through The Lens Of Leave-One-Out Error](https://mlsanity.com/paper/2203.03443/) (2022)
- [A Realistic Protocol for Evaluation of Weakly Supervised Object Localization](https://mlsanity.com/paper/2404.10034/) (2024)
- [Channel redundancy and overlap in convolutional neural networks with channel-wise NNK graphs](https://mlsanity.com/paper/2110.11400/) (2021)
- [GraphNNK -- Graph Classification and Interpretability](https://mlsanity.com/paper/2602.00753/) (2026)

## Abstract (arXiv)

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.