# Interpretable machine learning in Physics

arXiv: https://arxiv.org/abs/2203.08021 · HTML: https://mlsanity.com/paper/2203.08021/

## Key facts

- Cited by 8 papers in the map (data through 7 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 11 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [Beyond Cuts in Small Signal Scenarios -- Enhanced Sneutrino Detectability Using Machine Learning](https://mlsanity.com/paper/2108.03125/) (2021)

## Built on by (most cited first, 8 of 8)

- [Renormalizing Diffusion Models](https://mlsanity.com/paper/2308.12355/) (2023)
- [Interpretable Meta-Learning of Physical Systems](https://mlsanity.com/paper/2312.00477/) (2023)
- [Explainable AI for High Energy Physics](https://mlsanity.com/paper/2206.06632/) (2022)
- [A Detailed Study of Interpretability of Deep Neural Network based Top Taggers](https://mlsanity.com/paper/2210.04371/) (2022)
- [Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networks](https://mlsanity.com/paper/2501.05656/) (2025)
- [Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane](https://mlsanity.com/paper/2604.25885/) (2026)
- [Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks](https://mlsanity.com/paper/2307.05299/) (2023)
- [A Step Toward Interpretability: Smearing the Likelihood](https://mlsanity.com/paper/2501.07643/) (2025)

## Abstract (arXiv)

Adding interpretability to multivariate methods creates a powerful synergy for exploring complex physical systems with higher order correlations while bringing about a degree of clarity in the underlying dynamics of the system.