4.2 Multi-gateMixture-of-Experts 我们提出了一种新的MoE模型,该模型旨在捕获任务差异,而与共享底部的多任务模型相比,不需要太多的模型参数。 新模型称为Multi-gate Mixture-of-Experts (MMoE) model,其中的主要思想是用Eq 5中的MoE层替换Eq1中的共享底部网络f。更重要的是,我们为每个任务k添加一个单独的门控...
因此,论文中提出了一个Multi-gate Mixture-of-Experts(MMoE)的多任务学习结构。MMoE模型刻画了任务相关性,基于共享表示来学习特定任务的函数,避免了明显增加参数的缺点。 模型介绍 MMoE模型的结构(下图c)基于广泛使用的Shared-Bottom结构(下图a)和MoE结构,其中图(b)是图(c)的一种特殊情况,下面依次介绍。 image....
CTR预估 论文精读(十四)--MMOE: Multi-gate Mixture-of-Experts,程序员大本营,技术文章内容聚合第一站。
这类方法的代表是 MMOE: Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts下图直观展示了拆 head 的集中常见做法 MMOE 中的两个 M,第一个代表 multi-gate,第二个代表 multi-expert;multi-expert 比较好理解,从 ensemble 的角度来看,就是在做 bagging,而 gate 就是在...
[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。
adopted graph-based feature selection and proposed a multi-gate mixture-of-experts model for joint diagnosis of autism spectrum disorder and attention deficit hyperactivity disorder from resting-state functional MRI data [33]; Amyar et al. introduced a multitask learning workflow based on U-Net ...
本文提出的 Multi-gate Mixture-of-Experts(MMoE) 可以显式的从数据中建模任务关系。该方法采用在所有任务中共享专家子模型的 Mixture-of-Experts(MoE) 结构来进行多任务学习,同时还通过训练过的门网络来优化每个任务。 背景 初衷 很多公司尝试多任务模型有如下几个可能的初衷:[3] ...
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 also having 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...
In this paper, we present the practical problems and the lessons learned at short-video services from Kuaishou. In industry, a widely-used multi-task framework is the Mixture-of-Experts (MoE) paradigm, which always introduces some shared and specific experts for each task and then uses gate ...