How to Understand Limitations of Generative Networks
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866916109528072192 |
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| author | Das, Ranit Favaro, Luigi Heimel, Theo Krause, Claudius Plehn, Tilman Shih, David |
| author_facet | Das, Ranit Favaro, Luigi Heimel, Theo Krause, Claudius Plehn, Tilman Shih, David |
| contents | Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustrate their benefits for distribution-shifted jets, calorimeter showers, and reconstruction-level events. In all cases, the classifier weights make for a powerful test of the generative network, identify potential problems in the density estimation, relate them to the underlying physics, and tie in with a comprehensive precision and uncertainty treatment for generative networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_16774 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | How to Understand Limitations of Generative Networks Das, Ranit Favaro, Luigi Heimel, Theo Krause, Claudius Plehn, Tilman Shih, David High Energy Physics - Phenomenology Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustrate their benefits for distribution-shifted jets, calorimeter showers, and reconstruction-level events. In all cases, the classifier weights make for a powerful test of the generative network, identify potential problems in the density estimation, relate them to the underlying physics, and tie in with a comprehensive precision and uncertainty treatment for generative networks. |
| title | How to Understand Limitations of Generative Networks |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2305.16774 |