所以对这一类学习算法的特性有一个比较恰当的描述:Learning how to learn. Meta learning 作为一个思维框架,有很多不同的应用版本。我们首先用一个具体的例子解释这个过程,再由这个例子总结对框架一般性的思考。 顾名思义,Model-agnostic (模型无关) meta learning 将模型作为一个缺省的部分,也就是说可以代入任何...
However, existing deep learning algorithms perform poorly on new tasks. Meta-learning, known as learning to learn, is one of the effective techniques to overcome this issue. Meta-learning's generalization ability to unknown tasks is improved by employing prior knowledge to assist the learning of ...
meta-learning 也叫learning to learn,就是学会学习,其实想法很早就有了,大概上世纪 90 年代,因为 人工智能要普世,学会学习是一个很有魅力的方向,以及主动学习终生学习等。 既然要利用之前学到的东西,我们就需要元学习模型学习一个先验知识来帮助以后学习一个新的任务,这就导致很多元学习论文中会有 Task 或者 Epi...
图8 meta learning和机器学习的损失函数对比—引自参考1 注意meta learning虽然是“learn to learn”,但是在实际使用时仍然需要调参数,例如解优化问题\phi^*=argminL\left( \phi \right),找到一个好的learning algorithm,找到之后,这个learning algorithm可以用在一个新的任务上(此时不需要调参数,已经学到了最优...
Meta learning又称为learn to learn，是说让机器“学会学习”，拥有学习的能力。元学习的训练样本和测试...
Meta learning, also called “learning to learn,” is a subcategory of machine learning that trains artificial intelligence (AI) models to understand and adapt to new tasks on their own.
Meta Learning,叫做元学习或者 Learning to Learn 学会学习,包括Zero-Shot/One-Shot/Few-Shot 学习,模型无关元学习(Model Agnostic Meta Learning)和元强化学习(Meta Reinforcement Learning)。元学习是人工智能领域,继深度学习是人工智能领域,继深度学习 -> 深度强化学习、生成对抗之后,又一个重要的研究分支,也是是近...
As we learn a task, we keep learning about it while performing the task. What we learn and how we learn it varies during different stages of learning. Learning how to learn and adapt is a key property that enables us to generalize effortlessly to new settings. This is in contrast with ...
A survey of deep meta-learning作者:Mike Huisman, Jan N. van Rijn, Aske Plaat 摘要 Deep neural networks can achieve great successes when presented with large data sets and sufficient computational resources. However, their ability to learn new concepts quickly is limited. Meta-learning is one app...
end using a task objective reflective of the particular model and a meta objective for learning good update strategies. Importantly, gradients of the meta and task objectives include the memory write computations; through them, the controllerlearnsto change the memory paramet...