[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...
我们从这些工作中获得灵感,通过使用子网(专家)的集合来实现迁移学习,同时节省计算。 2.3 Multi-task Learning Applications(多任务学习应用) 由于分布式机器学习系统的发展 [13],许多大规模的现实世界应用程序都采用了基于 DNN 的多任务学习算法,并观察到了实质性的质量改进。 在多语言机器翻译任务上,模型参数共享,训练...
备注:本文学习资料主要来自 _An Overview of Multi-Task Learning in Deep Neural Networks,https://arxiv.org/abs/1706.05098 Reference [1] A Bayesian/information theoretic model of learning to learn via multiple task sampling. http://link.springer.com/article/10.1023/A:1007327622663 [2] Learning from...
Thung K, Wee C, "A Brief Review on Multi-Task Learning", Multimedia Tools and Applications, August 2018. Rich Caruana 给出的MTL定义:“MTL is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an ...
多任务学习(Multi-task learning)是和单任务学习(single-task learning)相对的一种机器学习方法。在机器学习领域,标准的算法理论是一次学习一个任务,也就是系统的输出为实数的情况。复杂的学习问题先被分解成理论上独立的子问题,然后分别对每个子问题进行学习,最后通过对子问题学习结果的组合建立复杂问题的数学模型。多...
Allen School of Computer Science & Engineering at the University of Washington 讲座题目:Passive and Active Multi-Task Representation Learning 讲座摘要:Representation learning has been widely used in many applications. In this talk, I will present our work which uncovers when and why representation ...
PhD Project - Multi-task Learning and Applications - developing novel learning systems and learning algorithms for multi-task learning at The University of Manchester, listed on FindAPhD.com
Ruder S, "An Overview of Multi-Task Learning in Deep Neural Networks", arXiv 1706.05098, June 2017 深度学习方面MTL总结: 按照隐层,MTL基本分两类:Hard sharing和Soft sharing Hard sharing在多任务之间共享隐层,降低over fitting的风险。“The more tasks we are learning simultaneously, the more our mod...
International Joint Conference on Computer Vision and Computer Graphics Theory and ApplicationsD. Masip, A` . Lapedriza, and J. Vitria`. Multitask learning - an application to incremental face recognition. In VISAPP(1), pages 585-590, 2008....
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...