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    摘要:对网络规模的数据进行训练可能需要数月时间。但大多数计算和时间都浪费在已经学习或无法学习的冗余和噪声点上。为加速训练,本文提出了ReducibleHoldoutLossSelection(RHOLOSS),一种简单但有原则的技术,近似地选择那些最能减少模型泛化损失的点进行训练。因此,rho损失缓解了现
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    Clearsuccessfulloginhistory:echo>/var/log/wtmpViewloginhistory:lastClearcommand'sinputhistory:history-c#temperarllysudoaptinstall-ywipewipe~/.
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    Clearsuccessfulloginhistory:echo>/var/log/wtmpViewloginhistory:lastClearcommand'sinputhistory:history-c