IEEE

Toward Robust Graph Semi-Supervised Learning Against Extreme Data Scarcity

Abstract: The success of graph neural networks (GNNs) in graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice. When only a few labeled nodes ...
IEEE

A Graph-Based Semi-Supervised Model for Flagging Suspicious Accounts in Anti-Money Laundering Systems

Abstract: Money laundering is challenging because of the scarcity of detection accuracy as well as high false positives. Current graph-based models tend to miss important antimoney laundering (AML) ...
Android Police

What is semi-supervised learning in ML?

Ben Khalesi writes about where artificial intelligence, consumer tech, and everyday technology intersect for Android Police. With a background in AI and Data Science, he’s great at turning geek speak ...
inc42

What Is Semi-Supervised Learning? Here’s All You Need to Know

What Is Semi-Supervised Learning? Semi-supervised learning is a powerful machine learning technique that combines the strengths of supervised and unsupervised learning. It leverages a small amount of ...
EurekAlert!

Outperforms state-of-the-art algorithms in deep learning tasks

Deep learning based semi-supervised learning algorithms have shown promising results in recent years. However, they are not yet practical in real semi-supervised learning scenarios, such as medical ...