MAML 是一个通用的「模型无关」(任何模型都能用)的元学习算法,构建适用于多任务的模型初始化表示(an internal representation that is broadly suitable for many tasks),这样在新任务上只需要微调模型就可以产生很好的效果。 If the internal representation is suitable to many tasks, simply fine-tuning the para...
goal-conditioned reinforcement learning techniques that leverage the structure of the provided goal space to learn many tasks significantly faster meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly curriculum and lifelong learning, where the problem requir...
Meta-learning aims to deliver an adaptive model that is sensitive to these underlying distribution changes, but requires many tasks during the meta-training process. In this paper, we propose a tAsk-auGmented actIve meta-LEarning (AGILE) method to efficiently adapt DNNs to new tasks by using a...
而这种定义下的Meta Learning是从 Tasks (任务族)中采样的学习片段上进行的(这种用于训练的task叫做 support set),从而产生一种基础学习算法,该算法在从该任务族中采样的新任务(query set )上表现良好。 在极限情况下,所有的训练片段都可以从单一任务中采样,即能够从单一任务中重新对tasks进行定义以达到获取support...
To overcome this issue, we show, for the first time, how to rapidly adapt model architectures to new tasks in a many-task many-hardware few-shot learning setup by integrating Model Agnostic Meta Learning (MAML) into the NAS flow. The proposed NAS method (H-Meta-NAS) is hardware-aware...
This step is to achieve minimal test errors for the tasks. 3.3. Testing with Nullspace-Consistent Adaptation Let Fω denote the meta-learned model. One may adapt Fω to a test sample y via minimizing LiSURE. However, as Algorithm 1: GT-Free Meta-Learning for CSR...
即:confronting learners with (1) an open-ended series of related yet novel tasks, within which (2) preciously encountered tasks identifiably reoccur (for related observations, see Anderson, 1990; O’Donnell et al., 2009). In the present work, we formal-ize this dual learning problem, and...
Next, they design a set of meta-parameters θ = p(θ|D_{meta-train}), which includes the necessary information about D_{meta-train} to solve the new tasks. Equation 4 Mathematically speaking, with the introduction of this intermediary variable θ, the full likelihood of parameters for the...
To benefit the learning of a new task, meta-learning has been proposed to transfer a well-generalized meta-model learned from various meta-training tasks. Existing meta-learning algorithms randomly sample meta-training tasks with a uniform probability, under the assumption that tasks are of equal ...
In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial. In this paper, we develop Meta-SGD, an SGD-like, easily trainable meta-learner that can ...