Science
Generative Models and Statistical Validation
Key Points
High Energy Physics - Phenomenology [Submitted on 28 May 2026] Title:Generative Models and Statistical Validation View PDF HTML (experimental)Abstract:Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and density estimators. In this work, we first introduce the underlying framework of modern generative networks and then discuss challenges in quantifying their accuracy, precision, and statistical power.
High Energy Physics - Phenomenology
[Submitted on 28 May 2026]
Title:Generative Models and Statistical Validation
View PDF HTML (experimental)Abstract:Generative machine learning has become an essential tool in theoretical and experimental physics, especially in the context of fast surrogates and density estimators. In this work, we first introduce the underlying framework of modern generative networks and then discuss challenges in quantifying their accuracy, precision, and statistical power.
Submission history
From: Sofia Palacios Schweitzer [view email][v1] Thu, 28 May 2026 18:25:40 UTC (322 KB)
Current browse context:
hep-ph
Change to browse by:
References & Citations
Loading...
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender
(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.