Google在2018年“Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts”文章提出Multi-Mixture-of-Experts(MMoE)模型,是对Geoffrey Hinton 提出的Mixture-of-Experts(MoE、One-gate MoE)的一种改进,而它们都是一种特殊的Hard Parameter Sharing模型。这三种模型结构如Fig13,其中(a...
[6] A Dirty Model for Multi-task Learning. Advances in Neural Information Processing Systems https://papers.nips.cc/paper/4125-a-dirty-model-for-multi-task-learning.pdf [7] Distributed Multi-task Relationship Learning http://arxiv.org/abs/16...
多任务学习(Multitask Learning)是一种推导迁移学习方法,主任务(main tasks)使用相关任务(related tasks)的训练信号(training signal)所拥有的领域相关信息(domain-specific information),做为一直推导偏差(inductive bias)来提升主任务(main tasks)泛化效果(generalization performance)的一种机器学习方法。多任务学习涉及多...
[2] Caruana. R. Multitask Learning: A Knowledge based Source of Inductive Bias. Proceedings of the Tenth International Conference on Machine Learning. 1993. [3] Baxter, J. (1997) A Bayesian / Information Theoretic Model of Learning to Learn via Multiple Task Sampling. Machine Learning. 28, ...
【多任务学习】An Overview of Multi-Task Learning in Deep Neural Networks,译自:http://sebastianruder.com/multi-task/1.前言在机器学习中,我们通常关心优化某一特定指标,不管这个指标是一个标准值,还是企业KPI。为了达到这个目标,我们训练单一模型或多个模型集合
从图二可以发现,单任务学习时,各个task任务的学习是相互独立的,多任务学习时,多个任务之间的浅层表示共享(shared representation)。 2、多任务学习的定义 多任务学习(Multitask learning)定义:基于共享表示(shared representation),把多个相关的任务放在一起学习的一种机器学习方法。
Multi-task learning is a machine learning method in which a model learns to solve multiple tasks simultaneously. The assumption is that by learning to complete multiple correlated tasks with the same…
Related(Main Task,Related tasks,LearningAlg)= 1 LearningAlg(Main Task||Related tasks)> LearningAlg(Main Task) (1) LearningAlg表示多任务学习采用的算法,公式(1):第一个公式表示,把Related tasks与main tasks放在一起学习,效果更好;第二个公式表示,基于related tasks,采用LearningAlg算法的多任务学习Main ta...
概括来讲,一旦发现正在优化多于一个的目标函数,你就可以通过多任务学习来有效求解(Generally, as soon as you find yourself optimizing more than one loss function, you are effectively doing multi-task learning (in contrast to single-task learning))。在那种场景中,这样做有利于想清楚我们真正要做的是什么...
概括来讲,一旦发现正在优化多于一个的目标函数,你就可以通过多任务学习来有效求解(Generally, as soon as you find yourself optimizing more than one loss function, you are effectively doing multi-task learning (in contrast to single-task learning))。在那种场景中,这样做有利于想清楚我们真正要做的是什么...