2024
AAAI
AAAI 2024
Data-Efficient Graph Learning
Abstract
Abstract My research strives to develop fundamental graph-centric learning algorithms to reduce the need for human supervision in low-resource scenarios. The focus is on achieving effective and reliable data-efficient learning on graphs, which can be summarized into three facets: (1) graph weakly-supervised learning; (2) graph few-shot learning; and (3) graph self-supervised learning.
Authors
Topics
Artificial Intelligence > Learning Paradigms > Few-Shot Learning
Machine Learning > Learning Types > Self-Supervised Learning
Machine Learning > Learning Types > Weakly Supervised Learning
Deep Learning > Architectures > Graph Neural Networks
Machine Learning > Learning Types > Few-Shot Learning
Deep Learning > Learning Types > Self-Supervised Learning
Deep Learning > Learning Types > Weakly Supervised Learning
Deep Learning > Learning Types > Few-Shot Learning