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。

最终的 loss 被乘以专家数量 ，这样即使专家数量变化，reduction 也能保持恒定。这是因为在均匀路由情况下 。

是一个超参数，用于调整辅助 reduction 的权重。论文中选择了 ，这个值足够大，可以确保负载均衡，同时又足够小，不会压倒主要的交叉熵目标（即主要的训练损失）。论文实验了从 到 的 值范围，发现 的值可以快速平衡负载，同时不会干扰训练损失。

A celebração anual do battle royale occurça em 20 de junho com muitos itens de destaque, como o Conjunto Masculino Além do Infinito e a Parede de Gelo votada globalmente pela comunidade

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就是先让不同的skilled单独计算reduction，然后再加权求和得到总体的loss。这意味着，每个qualified在处理特定样本的目标是独立于其他pro的权重。尽管仍然存在一定的间接耦合（因为其他skilled权重的变化可能会影响门控网络分配给pro的score）。如果门控网络和expert都使用这个新的reduction进行梯度下降训练，系统倾向于将每个样本分配给一个单一professional。当一个expert在给定样本上的的decline小于所有specialist的平均decline时，它对该样本的门控rating会增加；当它的表现不如平均loss时，它的门控rating会减少。这种机制鼓励pro之间的竞争，而不是合作，从而提高了学习效率和泛化能力。下面是一个示意图：

Lo stile di gestione del fondo è passivo, ossia mira a replicare la efficiency dell'indice sottostante detenendo asset nelle stesse proporzioni dell'indice. L'obiettivo è quello di ottenere i medesimi rendimenti dell'indice.

为了解决这个问题，论文提出了使用多个模型（即专家，specialist）去学习，使用一个门控网络（gating network）来决定每个数据应该被哪个模型去训练，这样就可以减轻不同类型样本之间的干扰。

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