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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.00838 |
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| _version_ | 1866915026970869760 |
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| author | Bieringer, Sebastian Diefenbacher, Sascha Kasieczka, Gregor Trabs, Mathias |
| author_facet | Bieringer, Sebastian Diefenbacher, Sascha Kasieczka, Gregor Trabs, Mathias |
| contents | Recently, combinations of generative and Bayesian machine learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated distribution originating from limited training statistics. The interpretation of a distribution-wide uncertainty however remains ill-defined. We show a clear scheme for quantifying the calibration of Bayesian generative machine learning models. For a Continuous Normalizing Flow applied to a low-dimensional toy example, we evaluate the calibration of Bayesian uncertainties from either a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights, to gauge their behaviour on unsteady distribution edges. Well calibrated uncertainties can then be used to roughly estimate the number of uncorrelated truth samples that are equivalent to the generated sample and clearly indicate data amplification for smooth features of the distribution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00838 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Calibrating Bayesian Generative Machine Learning for Bayesiamplification Bieringer, Sebastian Diefenbacher, Sascha Kasieczka, Gregor Trabs, Mathias Machine Learning Artificial Intelligence High Energy Physics - Phenomenology Recently, combinations of generative and Bayesian machine learning have been introduced in particle physics for both fast detector simulation and inference tasks. These neural networks aim to quantify the uncertainty on the generated distribution originating from limited training statistics. The interpretation of a distribution-wide uncertainty however remains ill-defined. We show a clear scheme for quantifying the calibration of Bayesian generative machine learning models. For a Continuous Normalizing Flow applied to a low-dimensional toy example, we evaluate the calibration of Bayesian uncertainties from either a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights, to gauge their behaviour on unsteady distribution edges. Well calibrated uncertainties can then be used to roughly estimate the number of uncorrelated truth samples that are equivalent to the generated sample and clearly indicate data amplification for smooth features of the distribution. |
| title | Calibrating Bayesian Generative Machine Learning for Bayesiamplification |
| topic | Machine Learning Artificial Intelligence High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2408.00838 |