在论文中，作者提到这个损失函数可能会导致专家网络之间的强烈耦合，因为一个专家网络的权重变化会影响到其他专家网络的reduction。这种耦合可能会导致多个专家网络被用于处理每条样本，而不是专注于它们各自擅长的子任务。为了解决这个问题，论文提出了重新定义损失函数的方法，以鼓励专家网络之间的相互竞争。

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• We reply to all these experiences and choose action the place necessary, around and together with banning gamers from our recreation. Any accounts that we find to have already been utilized for illegal, fraudulent, harassing, defamatory, threatening or abusive applications could also be referred to law enforcement authorities.

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如果一个多层网络用来训练不同的子任务，通常会有强烈的干扰效应，这会导致学习过程变慢和泛化能力差。这种干扰效应的原因在于，当网络试图同时学习多个子任务时，不同任务的学习过程可能会相互干扰。例如，学习一个子任务时对权重的调整可能会影响其他子任务的学习效果，因为这些权重变化会改变其他子任务的loss。这种相互影响使得网络在处理每个子任务时都试图最小化所有其他子任务的decline。

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通过这种 qualified dropout 策略，有效地减少了过拟合的风险，同时保持了模型在下游任务上的性能。这种正则化方法对于处理具有大量参数的稀疏模型特别有用，因为它可以帮助模型更好地泛化到未见过的数据。

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