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Titlebook: Domain Generalization with Machine Learning in the NOvA Experiment; Andrew T.C. Sutton Book 2023 The Editor(s) (if applicable) and The Aut

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樓主: vitamin-D
21#
發(fā)表于 2025-3-25 06:00:30 | 只看該作者
22#
發(fā)表于 2025-3-25 09:02:56 | 只看該作者
23#
發(fā)表于 2025-3-25 12:59:12 | 只看該作者
24#
發(fā)表于 2025-3-25 19:26:54 | 只看該作者
25#
發(fā)表于 2025-3-25 20:13:41 | 只看該作者
The Early Enamel Carious Lesion is a type of recurrent neural network that is well suited to the particle physics where the number of outgoing particles is not known a-priori and the energies of those particles are all physically linked to eachother.
26#
發(fā)表于 2025-3-26 03:11:16 | 只看該作者
Pam Denbesten,Robert Faller,Yukiko NakanoTM network is also asked to identify which domain each event belongs to, and is penalized if it is able to do so correctly. This method pushes the LSTM away from features that distinguish between the domains and toward a middle ground that is more representative of reality.
27#
發(fā)表于 2025-3-26 05:04:58 | 只看該作者
The 3-Flavor Analysis,en FD simulation and data is performed to find the minimum log-likelihood across the parameter space, and Feldman-Cousins (Phys Rev D 57:3873–3889, 1998) corrections are applied. With such a reliance on simulation and reconstruction techniques, we include many systematic uncertainties that are included in the fit as nuisance parameters.
28#
發(fā)表于 2025-3-26 09:00:01 | 只看該作者
29#
發(fā)表于 2025-3-26 14:19:19 | 只看該作者
Domain Generalization by Adversarial Training,TM network is also asked to identify which domain each event belongs to, and is penalized if it is able to do so correctly. This method pushes the LSTM away from features that distinguish between the domains and toward a middle ground that is more representative of reality.
30#
發(fā)表于 2025-3-26 19:05:26 | 只看該作者
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