[2] Learning from hints in neural networks. Journal of Complexity https://doi.org/10.1016/0885-064X(90)90006-Y [3] Multi-Task Feature Learning http://doi.org/10.1007/s10994-007-5040-8 [4] Model selection and estimation in regression wit...
[3] Multi-Task Feature Learning http://doi.org/10.1007/s10994-007-5040-8 [4] Model selection and estimation in regression with grouped variables [5] Taking Advantage of Sparsity in Multi-Task Learninghttp://arxiv.org/pdf/0903.1468 [6] A Dirty Model for Multi-task Learning. Advances in N...
[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 hints in neural networks. Journal of Complexityhttps://doi.org/10.1016/0885-064X(90)90006-Y [3] Multi-Task Feature Learninghttp...
其中,gk是第k个任务中组合experts结果的门控网络(gating network),注意每一个任务都有一个独立的门控网络。它的输入是input feature,输出就是所有Experts上的权重 一方面,因为gating networks通常是轻量级的,而且expert networks是所有任务共用,所以相对于论文中提到的一些baseline方法在计算量和参数量上具有优势 另一方面...
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...
Gaussian process multi-task learning oSparse & Low Rank Methods Asymmetric Multi-Task Learning Hierarchical_Multi_Task_Learning Asynchronous Multi-Task Learning HMTL: Hierarchical Multi-Task Learning Multi-task feature learning Multiplicative MultiTask Feature Learning (MMTFL) ...
task多任务learningmultifeaturewedenoteby Multi-TaskFeatureLearningAndreasArgyriouDepartmentofComputerScienceUniversityCollegeLondonGowerStreet,LondonWC1E6BT,UKa.argyriou@cs.ucl.ac.ukTheodorosEvgeniouTechnologyManagementandDecisionSciences,INSEAD,Bd.deConstance,Fontainebleau77300,Francetheodoros.evgeniou@insead.eduMassi...
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation简析,程序员大本营,技术文章内容聚合第一站。
Online learning for multi-task feature selection Multi-task feature selection (MTFS) is an important tool to learn the explanatory features across multiple related tasks. Previous MTFS methods fulfill thi... H Yang,I King,MR Lyu - Acm Conference on Information & Knowledge Management 被引量: ...
背景:只专注于单个模型可能会忽略一些相关任务中可能提升目标任务的潜在信息,通过进行一定程度的共享不同任务之间的参数,可能会使原任务泛化更好。广义的讲,只要loss有多个就算MTL,一些别名(joint learning,learning to learn,learning with auxiliary task)