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Titlebook: Job Scheduling Strategies for Parallel Processing; 18th International W Walfredo Cirne,Narayan Desai Conference proceedings 2015 Springer I

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書目名稱Job Scheduling Strategies for Parallel Processing
副標(biāo)題18th International W
編輯Walfredo Cirne,Narayan Desai
視頻videohttp://file.papertrans.cn/501/500995/500995.mp4
概述Includes supplementary material:
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Job Scheduling Strategies for Parallel Processing; 18th International W Walfredo Cirne,Narayan Desai Conference proceedings 2015 Springer I
描述This book constitutes the thoroughly refereed post-conference proceedings of the 18th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2014, held in Phoenix, AZ, USA, in May 2014. The 9 revised full papers presented were carefully reviewed and selected from 24 submissions. The papers cover the following topics: single-core parallelism; moving to distributed-memory, larger-scale systems, scheduling fairness; and parallel job scheduling.
出版日期Conference proceedings 2015
關(guān)鍵詞cloud computing; distributed systems; global earliest deadline first; grid computing; high performance c
版次1
doihttps://doi.org/10.1007/978-3-319-15789-4
isbn_softcover978-3-319-15788-7
isbn_ebook978-3-319-15789-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2015
The information of publication is updating

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Alexander Herz,Chris Pinkau. We further show how the exposed internals can be exploited to strengthen adversarial examples against the model. Our work starts an important discussion on the security implications of diagnosing deployed models with limited accessibility. The code is available at ..
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Rajath Kumar,Sathish Vadhiyar-end fashion. At the same time, we maximize the information-theoretic dependency between data and their predicted discrete representations. Our IMSAT is able to discover interpretable representations that exhibit intended invariance. Extensive experiments on benchmark datasets show that IMSAT produc
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Uwe Schwiegelshohnata, offering direct and comprehensible explanations by highlighting key features, deriving rules, and identifying cluster prototypes. The comparison with existing XAI frameworks like SHAP and ClAMP shows that we achieve similar fidelity and simplicity, proving that mixed data clusterings can be eff
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