The progress of the computer vision field is dependent on the large volume of labelled data,
and it is a challenge to replicate these successes in real tasks with few labelled data …

In this paper, we propose a meta-optimizer-based approach for probabilistic wind power
forecasting (WPF) with limited historical data, including offline training and online adaptation …

Popular few-shot Meta-learning (ML) methods presume that a task's support and query data
are drawn from a common distribution. Recently, Bennequin et al. relaxed this assumption to …

The prevailing literature typically assesses the effectiveness of meta-learning (ML)
approaches on tasks that involve no more than 20 classes. However, we challenge this …

This article provides a concise yet comprehensive overview of major areas in transfer
learning. As the use of existing knowledge to train machine learning models has gained …