Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-
learning or few-shot learning, aims to effectively train a model using only a small amount of …

Accurate blood glucose prediction is a critical element in modern artificial pancreas systems.
Recently, many deep learning-based models have been proposed for glucose prediction …

The success of meta-learning on out-of-distribution (OOD) tasks in the wild has proved to be
hit-and-miss. To safeguard the generalization capability of the meta-learned prior …

In this paper, we present a novel method for the automatic classification of medical images
that learns and leverages weak causal signals in the image. Our framework consists of a …

Although deep neural networks are capable of achieving performance superior to humans
on various tasks, they are notorious for requiring large amounts of data and computing …

Learning with limited labelled data, such as few-shot learning, meta-learning or transfer
learning, aims to effectively train a model using only small amount of labelled samples …

As a subset of machine learning, meta-learning, or learning to learn, aims at improving the
model's capabilities by employing prior knowledge and experience. A meta-learning …

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 …

Environment shifts and conflicts present significant challenges for learning-based sound
event localization and detection (SELD) methods. SELD systems, when trained in particular …

Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple
pre-trained models without requiring their original training data. Existing inversion-based …