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 …

Meta-learning empowers learning systems with the ability to acquire knowledge from
multiple tasks, enabling faster adaptation and generalization to new tasks. This review …

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 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 object detection, which aims at detecting novel objects rapidly from extremely few
annotated examples of previously unseen classes, has attracted significant research interest …

Abstract In Federated Learning, we aim to train models across multiple computing units
(users), while users can only communicate with a common central server, without …

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 …

Personalized federated learning is tasked with training machine learning models for multiple
clients, each with its own data distribution. The goal is to train personalized models …

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 …