IEEE

Multiscale Structural Information-Based Laplacian Generative Adversarial Network Representation Learning

Abstract: In recent years, network representation learning (NRL) has attracted increasing attention due to its efficiency and effectiveness to analyze network structural data. NRL aims to learn ...
Computer Weekly

Dutch researcher’s AI breakthrough tackles the structured data paradox

The project for which Hulsebos received the grant is called DataLibra, which runs from 2024 to 2029. Over those five years, ...
IEEE

Topological Data Analysis in Graph Neural Networks: Surveys and Perspectives

Abstract: For many years, topological data analysis (TDA) and deep learning (DL) have been considered separate data analysis and representation learning approaches, which have nothing in common. The ...
Journal of Medical Internet Research

Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models: Quantitative Study Using Large Language Models

Background: Data collected in controlled settings typically results in high-quality datasets. However, in real-world applications, the quality of data collection is often compromised. It is well ...