# Explaining Deep Learning Models - A Bayesian Non-parametric Approach

arXiv: https://arxiv.org/abs/1811.03422 · HTML: https://mlsanity.com/paper/1811.03422/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2018.

## Built on by (most cited first, 16 of 16)

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- [Mixture of Linear Models Co-supervised by Deep Neural Networks](https://mlsanity.com/paper/2108.04035/) (2021)
- [First Step Towards EXPLAINable DGA Multiclass Classification](https://mlsanity.com/paper/2106.12336/) (2021)
- [SynthTree: Co-supervised Local Model Synthesis for Explainable Prediction](https://mlsanity.com/paper/2406.10962/) (2024)
- [Linking Model Intervention to Causal Interpretation in Model Explanation](https://mlsanity.com/paper/2410.15648/) (2024)
- [Measuring Linguistic Diversity During COVID-19](https://mlsanity.com/paper/2104.01290/) (2021)
- [Bayesian Nonparametric Reinforcement Learning in LTE and Wi-Fi Coexistence](https://mlsanity.com/paper/2105.12249/) (2021)
- [Bayesian Nonparametric Modelling for Model-Free Reinforcement Learning in LTE-LAA and Wi-Fi Coexistence](https://mlsanity.com/paper/2107.02431/) (2021)
- [EBLIME: Enhanced Bayesian Local Interpretable Model-agnostic Explanations](https://mlsanity.com/paper/2305.00213/) (2023)
- [REALM: Regime-Switching, Explainable, and Activation-Induced Linear Models](https://mlsanity.com/paper/2609.32141/) (2026)

## Abstract (arXiv)

Understanding and interpreting how machine learning (ML) models make decisions have been a big challenge. While recent research has proposed various technical approaches to provide some clues as to how an ML model makes individual predictions, they cannot provide users with an ability to inspect a model as a complete entity. In this work, we propose a novel technical approach that augments a Bayesian non-parametric regression mixture model with multiple elastic nets. Using the enhanced mixture model, we can extract generalizable insights for a target model through a global approximation. To demonstrate the utility of our approach, we evaluate it on different ML models in the context of image recognition. The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.