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...
翻译自论文《2018-MMoE-Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts》 摘要 基于神经网络的多任务学习(multi-task learning)在大规模真实应用上的取得了成功,比如推荐系统。例如,在电影推荐系统,推荐系统不仅能预测用户会购买和观看哪些视频,还能预测用户未来会喜欢这个电影。
1 学习动机第一次了解到MoE(Mixture of experts),是在GPT-4模型架构泄漏事件,听说GPT-4的架构是8个GPT-3级别大小的模型以MoE架构(8*220B)组合成一个万亿参数级别的模型。不过在这之后… Verlo...发表于前沿技术学... multi-modality 技术初探 dghs 【简读】Dense-to-sparse Gate for Mixture-of-experts 20...
即 TowerA 的输入 size 等于 Expert 输出隐藏单元个数(在这个例子中,Expert 最后一层全连接层隐藏单元个数为2,因此 TowerA 的输入维度也为2),所以 TowerA 的输入为[ GA1∗ E01+ GA2∗ E11+
Multi-Gating Mixture Of Experts 最右侧就是本文提出的MMoE模型,不同任务共享底层的一组Bottom层,称为一组Expert,每个Experts可能善于捕捉部分数据和目标之间的关系。并且,每个任务会关联一个gating网络,这个gating网络输入和Experts层一样,输出层是一个softmax,每个权重和一个experts绑定。相当于每个任务对Experts层输出...
[论文笔记]Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts,程序员大本营,技术文章内容聚合第一站。
The mixture-of-experts architecture improves upon the shared-bottom model by creating multiple expert networks and adding a gating network to weight each expert network’s output. Source Each expert network is essentially a unique shared bottom network, each using the same network architecture. The ...
We adapt the Mixture-of-Experts (MoE) structure to multi-task learning by sharing the expert submodels across all tasks, while also having a gating network trained to optimize each task. To validate our approach on data with different levels of task relatedness, we first apply it to a ...
In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data. We adapt the Mixture-of-Experts (MoE) structure to multi-task learning by sharing the expert submodels across all tasks, while...
采用Mixture-of-Experts(MoE)结构,在不同的子任务间共享专家子模块(expert submodels),并且通过门控网络(gating network)对各个子任务进行优化。 2.3 实验结果 当子任务之间相关性较低的时候,MMoE模型效果会更好。同时,MMoE模型更容易训练。 2.4 关键词 multi-task learning; mixture of experts; neural network;...