Generative Models and Statistical Validation
arXiv:2605.3045360.61 citationsh-index: 13
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This work addresses the problem of validating generative models for physicists using them as fast surrogates and density estimators.
This paper reviews the framework of modern generative networks and discusses the challenges in quantifying their accuracy, precision, and statistical power within the context of theoretical and experimental physics.
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.