Rewriting Reality: Graph Diffusion for Explainable AI

A new framework leverages the power of graph diffusion models to generate realistic and actionable counterfactual explanations for graph-structured data.

A new framework leverages the power of graph diffusion models to generate realistic and actionable counterfactual explanations for graph-structured data.

This review explores how artificial intelligence is being leveraged to create more sustainable and adaptable next-generation wireless communication systems.

New research explores how synthetic data and advanced machine learning can overcome the challenges of identifying wildfires in real-world imagery.

Researchers have developed a novel graph-based framework to analyze transaction patterns in the privacy-focused cryptocurrency Monero, offering a path to detect illicit activity without breaking its core anonymity features.
A new deep learning framework combines advanced image analysis with explainable AI to improve both the accuracy and clinical understanding of diabetic retinopathy detection.

A new framework leverages the principles of information theory to quantify market efficiency and refine financial risk management.
As artificial intelligence reshapes the business world, this article provides a critical framework for accounting researchers to navigate the opportunities and challenges of this rapidly evolving landscape.

New research casts doubt on the ability of machine learning to consistently predict short-term binary option movements.
New research demonstrates how combining artificial intelligence with both technical and fundamental data can significantly improve currency market predictions.
A novel deep learning framework combines the strengths of convolutional, transformer, and graph neural networks to dramatically improve weed detection in agricultural settings.