To ensure good performance, modern machine learning models typically require large
amounts of quality annotated data. Meanwhile, the data collection and annotation processes …

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

In recent decades, wildfires have caused tremendous property losses, fatalities, and
extensive damage to forest ecosystems. Inspired by the abundance of publicly available …

Node classification is an essential problem in graph learning. However, many models
typically obtain unsatisfactory performance when applied to few-shot scenarios. Some …

As innovation in deep learning continues, many engineers want to adopt Pre-Trained deep
learning Models (PTMs) as components in computer systems. PTMs are part of a research-to …

In this work, we focus on the challenging task, neuro-disease classification, using functional
magnetic resonance imaging (fMRI). In population graph-based disease analysis, graph …

Despite deep learning's widespread success, its data-hungry and computationally
expensive nature makes it impractical for many data-constrained real-world applications …

Unsupervised meta-learning (UML) essentially shares the spirit of self-supervised learning
(SSL) in that their goal aims at learning models without any human supervision so that the …

Graph Neural Networks (GNNs) have become popular tools for Graph Representation
Learning (GRL). One fundamental problem is few-shot node classification. Most existing …

Authors' addresses: Wenxin Jiang, Purdue University, West Lafayette, IN, USA, jiang784@
purdue. edu; Chingwo Cheung, Purdue University, West Lafayette, IN, USA, cheung59 …