Few-shot learning (FSL) has emerged as an effective learning method and shows great
potential. Despite the recent creative works in tackling FSL tasks, learning valid information …

At present, the mechanisms of in-context learning in Transformers are not well understood
and remain mostly an intuition. In this paper, we suggest that training Transformers on auto …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

While large pre-trained models have enabled impressive results on a variety of downstream
tasks, the largest existing models still make errors, and even accurate predictions may …

Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms
that can continually learn new concepts from a few data points, without forgetting knowledge …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

In this paper, we address the few-shot classification task from a new perspective of optimal
matching between image regions. We adopt the Earth Mover's Distance (EMD) as a metric to …

The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …

Few-shot classification consists of a training phase where a model is learned on a relatively
large dataset and an adaptation phase where the learned model is adapted to previously …

Abstract Model-agnostic meta learning (MAML) is currently one of the dominating
approaches for few-shot meta-learning. Albeit its effectiveness, the optimization of MAML …