Titlebook: Innovationen in der Wirtschaft; Trends in Industrie, Sabrina Krauss,Philipp Plugmann Book 2022 Der/die Herausgeber bzw. der/die Autor(en),

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書目名稱Innovationen in der Wirtschaft影響因子(影響力)




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書目名稱Innovationen in der Wirtschaft網(wǎng)絡公開度




書目名稱Innovationen in der Wirtschaft網(wǎng)絡公開度學科排名




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書目名稱Innovationen in der Wirtschaft被引頻次學科排名




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書目名稱Innovationen in der Wirtschaft年度引用學科排名




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書目名稱Innovationen in der Wirtschaft讀者反饋學科排名

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ation essentially providesprogress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically.?.This book is written for anyone who wants to learn, understand, and apply the relationship between deep

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J?rn Littkemann,Christian Geyer,Sabrina Jungs learned and how it makes its decisions is no longer feasible [159]. Second, online learning systems have the capability to self-optimize their performance by changing their internal structure during operation. This leads to further difficulty for functional safety validation when online learning i

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on learning more multi-modal behaviors, such as lane keeping systems which can also change lane or turn when required. While the works discussed in this book present impressive results, these systems are still far from humanlevel performance. However, research in this field is ongoing and quickly ev

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Markus Dohm. Furthermore, we present a new Bayesian deep learning framework, called . (exVDP), for quantifying uncertainty in the decision of DNNs. In particular, exVDP provides a pixel-level uncertainty map associated with the network’s segmentation output. Finally, we present clinical retrospective studies i

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Arno Lammerts,Julia Ingwald. Furthermore, we present a new Bayesian deep learning framework, called . (exVDP), for quantifying uncertainty in the decision of DNNs. In particular, exVDP provides a pixel-level uncertainty map associated with the network’s segmentation output. Finally, we present clinical retrospective studies i

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Johannes Emmerich,Janine Lin?er. Furthermore, we present a new Bayesian deep learning framework, called . (exVDP), for quantifying uncertainty in the decision of DNNs. In particular, exVDP provides a pixel-level uncertainty map associated with the network’s segmentation output. Finally, we present clinical retrospective studies i