In the modern digital era, fraud in programmatic advertising has become a multi-billion-dollar challenge, threatening the ...

Studies leveraging NIR and CNNs report accuracies exceeding 95%, with some achieving perfect classification for pure fibers.

MIT researchers found that different algorithms can all be grouped into a ‘periodic table’ of AI. The idea for the table was ...

The increasing digitalization of banking services has led to a surge in financial fraud, necessitating advanced detection ...

The integration of data science into electrocatalysis has significantly advanced the discovery of high-performance catalysts for sustainable energy ...

Abstract: This research outlines the significance of semi-supervised machine learning (SSML) in dealing with the intricate ... generative models, and graph-based methods, highlighting their particular ...

However, SCN is mainly used for supervised learning and its performance is limited in the case of scarce labeled data. To this end, this paper proposes semi-supervised SCN (MR-SCN) in combination with ...

MIT researchers have recently unveiled an innovative framework that organizes artificial intelligence (AI) algorithms in a ...

Nevertheless, prior methods have predominantly centered on supervised learning, which necessitates a reliance on costly labeled datasets. In response to this challenge, graph contrastive learning ...

This repository contains code to the paper Self-Supervised Graph Representation Learning for Neuronal Morphologies by M.A. Weis, L. Hansel, T. Lüddecke and A.S. Ecker (2023). The training of GraphDINO ...

This study employs graph representation learning combined with classical machine learning techniques to model and interpret the structural evolution of LLM-related survey papers. By constructing ...