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...
更具体地,如果训练样本数为 1,则称为一次学习(One-Shot Learning);训练样本数为 K,称为 K 次学习;更极端地,训练样本数为 0,称为零次学习(Zero-Shot Learning)。另外,多任务学习(Multitask Learning)和迁移学习(Transfer Learning)在理论层面上都能归结到元学习的大家庭中。 当前的深度学习大部分情况下只能从...
这篇文章中将Multi-task learning 方法分为了两类:1)multi-task feature learning that learns a shared fearture representation; 2)multi-task relationship learning that models inherent task relationship 1. Motivation How to exploit the task relatedness underlying parameter tensors and improve feature transfer...
多任务学习-Multitask Learning ,忽略了问题之间所富含的丰富的关联信息。多任务学习就是为了解决这个问题而诞生的。把多个相关(related)的任务(task)放在一起学习。这样做真的有效吗?答案是肯定的。多个任务之间共享一些因素,它们... representation),把多个相关的任务放在一起学习的一种机器学习方法。多任务学习(Mult...
【论文笔记】多任务学习(Multi-Task Learning) 1. 前言 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,...
Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task...
另一个可以通过度量学习或表示学习大量改进的问题是灾难遗忘问题， 连续学习的克星。对于相关性强的task...
We apply a recently proposed technique - Multi-task Multi-Kernel Learning (MTMKL) - to the problem of modeling students' wellbeing. Because wellbe- ing is a complex internal state consisting of several related dimensions, Multi-task learning can be used to classify them simultaneously. Multiple...
Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task...
Despite the recent progress in deep learning, most approaches still go for a silo-like solution, focusing on learning each task in isolation: training a separate neural network for each individual task. Many real-world problems, however, call for a multi-modal approach and, therefore, for multi...