可以聊聊专家混合模型（mixture of expert）吗?传统的MoE架构用 MoE 层替代Transformer中的前馈网络 (FFN...
MoE，全称为Mixed Expert Models，翻译过来就是混合专家模型。MoE并不是什么最新技术，早在1991年的时候...
Training an MoE model involves optimizing both the expert models and the gating mechanism. Each expert is trained on a different subset of the overall training data, enabling these models to develop specialized knowledge bases and problem-solving capabilities. Meanwhile, the gating mechanism...
输出就是所有 experts 的加权和:(跟第一篇论文的第一个公式类似)但是这里我们可能有上千个 experts,如果每个都算的话,计算量会非常大,所以这里的一个关键就是希望 G(x) 的输出是稀疏的,只有部分的 experts 的权重是大于 0 的,其余等于 0 的 expert 直接...
Frequency-domain attention mixture of expert models for remaining useful life prediction of lithium-ion batteriesdoi:10.1007/s11760-024-03488-4Lithium-ion batteryRemaining useful lifeDeep learningAttention mechanismMixture of expertsThe ability to accurately predict the remaining useful life (RUL) of ...
Mixture-of-Experts(MoE) MoE模型可以形式化表示为y=∑ni=1gi(x)fi(x) , 其中∑ni=1gi(x)=1,且fi,i=1,...,n是n个expert network(expert network可认为是一个神经网络)。 g是组合experts结果的gating network,具体来说g产生n个experts上的概率分布,最终的输出是所有experts的带权加和。显然,MoE可看做...
就是让不同的 expert 单独计算 loss,然后在加权求和得到总体的 loss。这样的话,每个专家,都有独立判断的能力,而不用依靠其他的 expert 来一起得到预测结果。下面是一个示意图: MoE 在这种设计下,我们将 experts 和 gating network 一起进行训练,最终的系统就会倾向于让一个 expert 去处理一个样本。
Mixture of experts (MoE) is a machine learning approach, diving an AI model into multiple “expert” models, each specializing in a subset of the input data.
The statistical properties of the likelihood ratio test statistic (LRTS) for mixture-of-expert models are addressed in this paper. This question is essential when estimating the number of experts in the model. Our purpose is to extend the existing results for simple mixture models (Liu and Shao...
In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for managing computational costs when scaling up model parameters. However, conventional MoE architectures like GShard, which activate the top-$K$ out of...