mlsanityData to 7 Oct 2026

Interpretable machine learning in Physics

arXiv 2203.08021 ↗First posted 11 Mar 2022hep-ph cs.LGcited by 8

Not on the map. Why?

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.

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

5 of 14 references.

References · 5

  1. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,040
  3. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
  4. 2019Interpretable machine learning: definitions, methods, and applicationscited by 303
  5. 2021Beyond Cuts in Small Signal Scenarios -- Enhanced Sneutrino Detectability Using Machine Learningcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 0; 2025: 2; 2026: 1.

8 papers.

  1. 2023Renormalizing Diffusion Modelscited by 16
  2. 2023Interpretable Meta-Learning of Physical Systemscited by 11
  3. 2022Explainable AI for High Energy Physicscited by 7
  4. 2022A Detailed Study of Interpretability of Deep Neural Network based Top Taggerscited by 6
  5. 2025Evidential Deep Learning for Uncertainty Quantification and Out-of-Distribution Detection in Jet Identification using Deep Neural Networkscited by 3
  6. 2026Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Planecited by 3
  7. 2023Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networkscited by 1
  8. 2025A Step Toward Interpretability: Smearing the Likelihoodcited by 1